Experiment 3: Document at various level for datasets(COVID-science : non-science : Psychology)

1. Article level

#importing libraries
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel

C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.
  warnings.warn(msg)

LSA on covid-science.txt at article level

def load_data(path,file_name,seperator):
    """
    Input  : path and file_name
    function: loading text file
    Output : number of words, document list
    """
    
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            i=i+1
            if text !='':
                document = document+text
                if text == seperator:
                    documents_list.append(document)
                    document = ''
        documents_list.append(document)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science.txt",'NEW_PAPER')

Number of words in text file : 120257
Total Number of Documents: 20

#data preprocessing/cleaning
def preprocess_data(doc_set):
    """
    Input  : list of documents
    function: text preprocessing - word tokenize, removing english stopwords, and potter stemming 
    Output : clean text
    """
    # initialize regex tokenizer
    word_tokenizer = RegexpTokenizer(r'\w+')
    # create English stop words list
    eng_stop = set(stopwords.words('english'))
    # Create p_stemmer of class PorterStemmer
    po_stemmer = PorterStemmer()
    # list for tokenized documents in loop
    texts = []
    # loop through document list
    for i in doc_set:
        # clean and tokenize document string
        raw = i.lower()
        tokens = word_tokenizer.tokenize(raw)
        tokens = [i for i in tokens if len(i)>3]
        print(tokens)
        # removing english stop words from tokens
        stopped_tokens = [i for i in tokens if not i in eng_stop]
        # stemmed tokens
        stem_tokens = [po_stemmer.stem(i) for i in stopped_tokens]
        # list of tokens
        texts.append(stem_tokens)
    return texts

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'many', 'cases', 'imagesbecome', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'such', 'systems', 'they', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levelsmanuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date', 'publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajalprogramme', '2015', '18136', 'gómez', 'ríos', 'supported', 'thefpu', 'programme', 'fpu16', '04765', 'charte', 'supported', 'fpuprogramme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'researchcouncil', 'grant', 'agreement', '647038', 'biodesert', 'this', 'projectis', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'with', 'andalusian', 'research', 'institute', 'indata', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínicosan', 'cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'area', 'with', 'atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'vigo', 'galicia', 'spain', 'mail', 'mreyarea', 'gmail', 'guirado', 'with', 'multidisciplinary', 'institute', 'environmentstudies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain', 'mail', 'geesecillo', 'gmail', 'digital', 'object', 'identifier', '1109', 'jbhi', '2020', '3037127of', 'severity', 'from', 'normal', 'with', 'positive', 'mild', 'moderate', 'severe', 'covidgr', 'contains', 'positive', 'and426', 'negative', 'posteroanterior', 'views', 'wepropose', 'covid', 'smart', 'data', 'based', 'network', 'covid', 'sdnet', 'methodology', 'improving', 'generalization', 'capacity', 'ofcovid', 'classification', 'models', 'approach', 'reaches', 'goodand', 'stable', 'results', 'with', 'accuracy', 'severe', 'moderate', 'andmild', 'covid', 'severity', 'levels', 'approach', 'could', 'help', 'inthe', 'early', 'detection', 'covid', 'covidgr', 'along', 'withthe', 'severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'this', 'link', 'https', 'dasci', 'transferencia', 'open', 'data', 'covidgr', 'index', 'terms', 'covid', 'convolutional', 'neural', 'networks', 'smart', 'data', 'introductionin', 'last', 'months', 'world', 'been', 'witnessing', 'howcovid', 'pandemic', 'increasingly', 'infecting', 'large', 'massof', 'people', 'very', 'fast', 'everywhere', 'world', 'trends', 'arenot', 'clear', 'some', 'research', 'confirm', 'that', 'this', 'problem', 'maypersist', 'until', '2024', 'besides', 'prevalence', 'studies', 'conducted', 'inseveral', 'countries', 'reveal', 'that', 'tiny', 'proportion', 'populationhave', 'developed', 'antibodies', 'after', 'exposure', 'virus', 'spain', 'this', 'means', 'that', 'frequently', 'large', 'number', 'patientswill', 'need', 'assessed', 'small', 'time', 'intervals', 'numberof', 'clinicians', 'with', 'very', 'resources', 'general', 'covid', 'diagnosis', 'carried', 'using', 'leastone', 'these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment', 'itconsists', 'analyzing', 'radiographic', 'images', 'from', 'different', 'angles', 'needed', 'equipment', 'this', 'assessmentis', 'available', 'most', 'hospitals', 'takes', 'more', 'than15', 'minutes', 'patient', 'addition', 'time', 'required', 'forct', 'decontamination', '2reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'test', 'detects', 'viral', 'from', 'sputum', 'online', 'available', 'https', 'english', 'elpais', 'society', '2020', 'antibody', 'study', 'shows', 'just', 'spaniards', 'have', 'contracted', 'coronavirus', 'html', 'online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection', 'ieee', '2020', 'this', 'article', 'free', 'access', 'download', 'along', 'with', 'rights', 'full', 'text', 'data', 'mining', 'analysis', 'authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'july', '2021', 'from', 'ieee', 'xplore', 'restrictions', 'apply', '3596', 'ieee', 'journal', 'biomedical', 'health', 'informatics', 'december', '2020fig', 'stratification', 'radiological', 'severity', 'covid', 'examples', 'rale', 'index', 'calculated', 'nasopharyngeal', 'swab', 'requires', 'specific', 'material', 'andequipment', 'which', 'easily', 'accessible', 'takes', 'atleast', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'soon', 'possible', 'some', 'studies', 'found', 'that', 'results', 'from', 'severaltests', 'different', 'points', 'from', 'same', 'patients', 'were', 'variable', 'during', 'course', 'illness', 'producing', 'high', 'falsenegative', 'rate', 'authors', 'suggested', 'that', 'testshould', 'combined', 'with', 'other', 'clinical', 'tests', 'such', 'chest', 'required', 'equipment', 'thisassessment', 'less', 'cumbersome', 'lightweightand', 'transportable', 'general', 'this', 'type', 'resources', 'moreavailable', 'than', 'required', 'scan', 'tests', 'addition', 'test', 'takes', 'about', 'seconds', 'patient', 'which', 'makes', 'most', 'time', 'cost', 'effectiveassessment', 'tools', 'recent', 'studies', 'provide', 'estimates', 'expert', 'radiologistssensitivity', 'diagnosis', 'covid', 'based', 'scans', 'study', 'patients', 'with', 'chestct', 'essay', 'performed', 'within', 'days', 'reported', 'asensitivity', 'compared', 'with', 'sensitivityof', 'different', 'study', 'patients', 'mean', 'age56', 'years', 'reported', 'sensitivity', 'comparedwith', 'initial', 'according', 'analysis', '636ambulatory', 'patients', 'most', 'patients', 'presenting', 'urgent', 'carecenters', 'with', 'confirmed', 'coronavirus', 'disease', '2019', 'have', 'normal', 'ormildly', 'abnormal', 'findings', 'only', 'these', 'patientsare', 'correctly', 'diagnosed', 'expert', 'recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 'level', 'severity', 'adapting', 'previously', 'definedradiographic', 'assessment', 'lung', 'edema', 'rale', 'score', 'tocovid', 'this', 'score', 'calculated', 'assigning', 'valuebetween', 'each', 'lung', 'depending', 'extent', 'visualfeatures', 'such', 'consolidation', 'ground', 'glass', 'opacities', 'thefour', 'parts', 'each', 'lung', 'depicted', 'based', 'this', 'score', 'experts', 'identify', 'level', 'severity', 'infection', 'amongfour', 'severity', 'stages', 'normal', 'mild', 'moderate', 'andsevere', 'practice', 'patient', 'classified', 'expert', 'radiologistauthorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'july', '2021', 'from', 'ieee', 'xplore', 'restrictions', 'apply', 'tabik', 'covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', '3597as', 'normal', 'have', 'positive', 'refer', 'these', 'cases', 'asnormal', 'expert', 'annotation', 'adopted', 'this', 'work', 'basedin', 'this', 'score', 'automated', 'image', 'analysis', 'deep', 'learning', 'modelshave', 'great', 'potential', 'optimize', 'role', 'imagesfor', 'fast', 'diagnosis', 'covid', 'robust', 'accurate', 'dlmodel', 'could', 'serve', 'triage', 'method', 'support', 'formedical', 'decision', 'making', 'increasing', 'number', 'recent', 'worksclaim', 'achieving', 'impressive', 'sensitivities', 'higher', 'thanexpert', 'radiologists', 'these', 'high', 'sensitivities', 'biasin', 'most', 'used', 'covid', 'dataset', 'covid', 'image', 'datacollection', 'this', 'dataset', 'includes', 'very', 'small', 'number', 'ofcovid', 'positive', 'cases', 'coming', 'from', 'highly', 'heterogeneoussources', 'least', 'countries', 'most', 'cases', 'severe', 'patients', 'issue', 'that', 'drastically', 'reduces', 'clinical', 'value', 'populatenon', 'covid', 'healthy', 'classes', 'researchers', 'usingcxr', 'images', 'from', 'diverse', 'pulmonary', 'disease', 'repositories', 'theobtained', 'models', 'will', 'have', 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# preparing corpus for LSA
def prepare_corpus(doc_clean):
    """
    Input  : preprossed document
    function: creating term dictionary and document term matrix
    Output : term dictionary and Document Term Matrix
    """
    # every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    term_dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    docu_term_matrix = [term_dictionary.doc2bow(doc) for doc in doc_clean]
    # generate LDA model
    return term_dictionary,docu_term_matrix

def create_gensim_lsa_model(doc_clean,number_of_topics,words):
    """
    Input  : preprocessed document, number of topics and word count
    function: create LSA model using gensim
    Output : return LSA model
    """
    term_dictionary,docu_term_matrix=prepare_corpus(doc_clean)
    # LSA model
    lsamodel = LsiModel(docu_term_matrix, num_topics=number_of_topics, id2word = term_dictionary)  # train model
    return lsamodel

# LSA Model
number_of_topics=20
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_covid_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_articles = create_gensim_lsa_model(clean_text,number_of_topics,words)

#top 5 words from lsa
dict(model_science.show_topic(0, topn=5))

{'covid': 0.4819559123739421,
 'imag': 0.3305244842801084,
 'data': 0.25927028676543307,
 'use': 0.23373910286534985,
 'model': 0.2237104216675572}

#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph12.png')

# cosinesimilarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled15.png')

cosine-similarity
           covid       imag       data        use      model
covid          1    0.19987    0.36023   0.315797  0.0639177
imag     0.19987          1  -0.115461  0.0796236  -0.101251
data     0.36023  -0.115461          1 -0.0217574  0.0207809
use     0.315797  0.0796236 -0.0217574          1  0.0631737
model  0.0639177  -0.101251  0.0207809  0.0631737          1

#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig15.png')

LSA on covid-non-science.txt at article level

#loading document
document_list,titles,word_count_nscience=load_data("","covid-non-science.txt",'NEW NEWSPAPER')

Number of words in text file : 8713
Total Number of Documents: 20

#data preprocessing
clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'realityby', 'reuterspublished', 'july', '2020', 'updated', 'july', '2020by', 'marc', 'joneslondon', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes', 'have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return', 'infection', 'rates', 'which', 'make', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june', 'euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume', 'more', 'than', 'three', 'dozen', 'states', 'increases', 'covid', 'cases', 'with', 'cases', 'florida', 'spiking', 'above', 'nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'view', 'that', 'overall', 'situation', 'still', 'improving', 'overall', 'year', 'german', 'government', 'bond', 'yields', 'basis', 'points', 'this', 'week', 'their', 'biggest', 'weekly', 'rise', 'month', 'though', 'they', 'nudged', 'down', 'friday', 'riskier', 'italian', 'yields', 'fell', 'well', 'though', 'which', 'their', 'lowest', 'since', 'late', 'march', 'prices', 'also', 'eased', 'after', 'otherwise', 'solid', 'week', 'brent', 'crude', 'fell', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'both', 'were', 'around', 'this', 'time', 'months', 'copper', 'prices', 'were', 'poised', 'seventh', 'consecutive', 'weekly', 'gain', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply', 'three', 'month', 'copper', 'hovering', 'tonne', 'more', 'than', 'from', 'lows', 'ploughed', 'march', 'issue', 'that', 'hangs', 'over', 'markets', 'will', 'surge', 'secondary', 'infections', 'that', 'will', 'trigger', 'second', 'wave', 'national', 'rather', 'than', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote', 'note', 'reporting', 'marc', 'jones', 'newspaper']
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'than', 'eight', 'people', 'largely', 'forbidden', 'various', 'restrictions', 'also', 'apply', 'foreign', 'nationals', 'entering', 'sweden', 'domestic', 'travel', 'belgium', 'shops', 'hairdressers', 'swimming', 'pools', 'schools', 'open', 'although', 'secondary', 'students', 'only', 'school', 'half', 'time', 'beauty', 'parlours', 'reopen', 'from', 'march', 'cafes', 'restaurants', 'shut', 'essential', 'foreign', 'travel', 'banned', 'until', 'april', 'working', 'from', 'home', 'mandatory', 'where', 'possible', 'austria', 'lockdown', 'eased', 'this', 'month', 'despite', 'high', 'infections', 'shops', 'hairdressers', 'open', 'schools', 'have', 'person', 'lessons', 'with', 'daily', 'testing', 'nighttime', 'curfew', 'place', 'there', 'restrictions', 'leaving', 'tyrol', 'province', 'because', 'outbreak', 'south', 'african', 'variant', 'easing', 'measures', 'planned', 'until', 'easter', 'earliest', 'hungary', 'curfew', 'effect', 'with', 'exceptions', 'essential', 'move', 'work', 'health', 'reasons', 'walking', 'close', 'home', 'restaurants', 'closed', 'except', 'takeout', 'delivery', 'large', 'public', 'gatherings', 'allowed', 'cultural', 'venues', 'closed', 'with', 'events', 'online', 'only', 'secondary', 'schools', 'universities', 'closed', 'elementary', 'schools', 'open', 'weekly', 'testing', 'mandated', 'healthcare', 'workers', 'teachers', 'reporting', 'reuters', 'bureaus', 'compiled', 'mark', 'john', 'editing', 'larry', 'king', 'newspaper']
['nears', 'covid', 'deathsby', 'afppublished', 'february', '2021', 'updated', 'february', '2021the', 'united', 'states', 'brink', 'sunday', 'grim', 'milestone', 'covid', 'related', 'deaths', 'since', 'start', 'pandemic', 'nation', 'virus', 'expert', 'warned', 'semblance', 'normalcy', 'return', 'until', 'year', 'signs', 'hope', 'were', 'emerging', 'rollout', 'vaccines', 'dropping', 'massive', 'winter', 'spike', 'infections', 'heavy', 'toll', 'continues', 'mount', 'nation', 'that', 'reported', 'most', 'fatalities', 'cases', 'world', 'terrible', 'historic', 'haven', 'seen', 'anything', 'even', 'close', 'this', 'well', 'over', 'hundred', 'years', 'since', '1918', 'pandemic', 'influenza', 'anthony', 'fauci', 'chief', 'medical', 'advisor', 'president', 'biden', 'said', 'meet', 'press', 'something', 'that', 'stunning', 'when', 'look', 'numbers', 'almost', 'unbelievable', 'true', 'added', 'toll', 'johns', 'hopkins', 'university', 'tracking', 'website', 'stood', 'some', 'after', 'first', 'covid', 'death', 'announced', 'united', 'states', 'february', '2020', 'took', 'about', 'three', 'months', 'pass', 'mark', 'during', 'first', 'wave', 'that', 'york', 'particularly', 'hard', 'outbreak', 'spread', 'across', 'country', 'pace', 'deaths', 'increased', 'with', 'toll', 'jumping', 'from', 'just', 'over', 'month', 'amid', 'spike', 'fueled', 'part', 'holiday', 'gatherings', 'fauci', 'noted', 'that', 'number', 'daily', 'infections', 'steep', 'decline', 'after', 'peaking', 'january', 'added', 'normal', 'life', 'still', 'some', 'think', 'have', 'significant', 'degree', 'normality', 'into', 'fall', 'winter', 'year', 'fauci', 'said', 'state', 'union', 'damage', 'done', 'pandemic', 'biden', 'said', 'last', 'week', 'program', 'deliver', 'vaccines', 'into', 'people', 'arms', 'complicated', 'already', 'intense', 'challenges', 'manufacturing', 'them', 'huge', 'quantities', 'speed', 'there', 'never', 'ever', 'ever', 'been', 'logistical', 'challenge', 'consequential', 'what', 'trying', 'getting', 'done', 'said', 'according', 'biden', 'goal', 'administering', 'million', 'shots', 'total', 'million', 'first', 'days', 'presidency', 'track', 'easily', 'surpassed', 'with', 'current', 'average', 'million', 'vaccinations', 'with', 'death', 'toll', 'soon', 'pass', 'biden', 'said', 'want', 'give', 'firm', 'predictions', 'when', 'crisis', 'will', 'curbed', 'however', 'said', 'that', 'million', 'doses', 'enough', 'provide', 'dose', 'regimen', 'most', 'country', 'were', 'expected', 'ready', 'july', 'more', 'than', 'million', 'people', 'have', 'received', 'least', 'shot', 'vaccine', 'united', 'states', 'with', 'some', 'million', 'getting', 'full', 'doses', 'freezing', 'weather', 'snow', 'storms', 'that', 'united', 'states', 'over', 'week', 'have', 'slowed', 'nation', 'vaccine', 'efforts', 'with', 'some', 'million', 'doses', 'delayed', 'gotten', 'million', 'doses', 'project', 'that', 'middle', 'week', 'will', 'have', 'caught', 'said', 'fauci', 'meet', 'press', 'calling', 'temporary', 'setback', 'though', 'concern', 'grown', 'around', 'variants', 'virus', 'especially', 'those', 'that', 'appear', 'spread', 'more', 'easily', 'render', 'current', 'shots', 'less', 'potent', 'fauci', 'sounded', 'hopeful', 'note', 'whether', 'another', 'surge', 'coming', 'think', 'that', 'inevitable', 'told', 'vaccines', 'currently', 'distributing', 'moderna', 'pfizer', 'vaccines', 'work', 'very', 'well', 'against', 'variant', 'america', 'might', 'past', 'worst', 'pandemic', 'damage', 'deep', 'painful', 'illustrate', 'this', 'milestone', 'half', 'million', 'deaths', 'york', 'times', 'published', 'front', 'page', 'graphic', 'running', 'length', 'page', 'with', 'each', 'small', 'point', 'representing', 'dead', 'american', 'bottom', 'column', 'which', 'represents', 'deaths', 'recent', 'months', 'particularly', 'dark', 'almost', 'uniformly', 'black', 'newspaper']
['covid', 'restrictions', 'easing', 'nswby', 'australian', 'associated', 'presspublished', 'september', '2020', 'updated', 'september', '2020nsw', 'easing', 'covid', 'restrictions', 'around', 'school', 'community', 'life', 'lower', 'community', 'acquired', 'covid', 'case', 'numbers', 'weddings', 'people', 'official', 'wedding', 'party', 'take', 'dance', 'floor', 'where', 'previously', 'only', 'bride', 'groom', 'were', 'permittedschools', 'interschool', 'activities', 'resume', 'saturday', 'with', 'covid', 'safety', 'plan', 'school', 'sport', 'will', 'return', 'normal', 'next', 'term', 'parents', 'school', 'sites', 'playing', 'wind', 'instruments', 'singing', 'chanting', 'permitted', 'with', 'strict', 'restrictions', 'year', 'formals', 'graduations', 'permitted', 'high', 'school', 'formals', 'permitted', 'after', 'kindergarten', 'orientation', 'back', 'year', 'transition', 'back', 'excursions', 'camps', 'resume', 'with', 'strict', 'guidelines', 'school', 'choirs', 'musical', 'ensembles', 'resume', 'long', 'players', 'keep', 'metre', 'distance', 'from', 'another', 'three', 'metres', 'players', 'reeded', 'woodwind', 'instruments', 'community', 'languages', 'schools', 'program', 'will', 'resume', 'face', 'face', 'learning', 'sport', 'more', 'than', 'parent', 'attend', 'community', 'sporting', 'activities', 'physical', 'distancing', 'least', 'metres', 'maintained', 'accommodation', 'facilities', 'overnight', 'event', 'organisers', 'must', 'implement', 'covid', 'safety', 'plan', 'carpooling', 'should', 'continue', 'avoidednew', 'newspaper']
['zealand', 'launches', 'covid', 'diary', 'appby', 'australian', 'associated', 'presspublished', '2020', 'updated', '2020belatedly', 'cases', 'covid', 'ease', 'zealand', 'government', 'launched', 'covid', 'help', 'kiwis', 'track', 'their', 'movements', 'prime', 'minister', 'jacinda', 'ardern', 'launched', 'wednesday', 'labelling', 'digital', 'diary', 'differs', 'from', 'australian', 'version', 'linking', 'other', 'users', 'instead', 'allows', 'kiwis', 'check', 'different', 'venues', 'such', 'cafes', 'restaurants', 'avoid', 'onerous', 'writing', 'down', 'contact', 'details', 'every', 'place', 'visited', 'helps', 'users', 'when', 'they', 'about', 'keep', 'their', 'movements', 'ardern', 'said', 'this', 'that', 'people', 'that', 'keeps', 'data', 'themselves', 'rather', 'than', 'adding', 'into', 'more', 'broader', 'repository', 'that', 'might', 'held', 'business', 'this', 'stage', 'data', 'sent', 'government', 'authorities', 'from', 'though', 'that', 'functionality', 'will', 'added', 'update', 'next', 'month', 'different', 'functionality', 'means', 'there', 'user', 'threshold', 'required', 'useful', 'still', 'plenty', 'kiwis', 'have', 'taken', 'even', 'before', 'officially', 'launched', 'kiwis', 'almost', 'cent', 'population', 'downloaded', 'wednesday', 'director', 'general', 'health', 'ashley', 'bloomfield', 'announced', 'there', 'were', 'cases', 'covid', 'zealand', 'fourth', 'there', 'have', 'been', 'just', 'cases', 'past', 'week', 'overall', 'past', 'fortnight', 'ever', 'decreasing', 'numbers', 'will', 'bars', 'nightclubs', 'open', 'first', 'time', 'since', 'march', 'thursday', 'provided', 'customers', 'seated', 'groups', 'under', 'receive', 'table', 'service', 'ardern', 'will', 'further', 'review', 'caps', 'gathering', 'numbers', 'currently', 'except', 'funerals', 'which', 'monday', 'cabinet', 'meeting', 'plan', 'regularly', 'adjust', 'settings', 'allow', 'more', 'activity', 'when', 'data', 'says', 'safe', 'ardern', 'said', 'showing', 'well', 'virus', 'been', 'squashed', 'over', 'past', 'months', 'just', 'zealander', 'being', 'treated', 'hospital', 'covid', 'does', 'require', 'intensive', 'care', 'newspaper']
['records', 'covid', 'cases', 'deathsby', 'reuterspublished', 'june', '2021', 'updated', 'june', '2021london', 'june', 'reuters', 'britain', 'reported', 'daily', 'covid', 'cases', 'government', 'figures', 'showed', 'thursday', 'down', 'slightly', 'from', 'reported', 'before', 'which', 'highest', 'daily', 'total', 'since', 'late', 'february', 'britain', 'also', 'reported', 'seven', 'further', 'deaths', 'within', 'days', 'positive', 'covid', 'test', 'from', 'wednesday', 'taking', 'total', 'death', 'toll', 'this', 'measure', 'some', 'british', 'adult', 'population', 'have', 'received', 'doses', 'covid', 'vaccine', 'have', 'received', 'least', 'dose', 'reporting', 'david', 'milliken', 'editing', 'kate', 'holton', 'newspaper']
['drugs', 'wrongly', 'touted', 'covid', 'treatmentsby', 'australian', 'associated', 'presspublished', 'august', '2020', 'updated', 'august', '2020medicines', 'falsely', 'touted', 'covid', 'treatments', 'hydroxychloroquine', 'used', 'treat', 'malaria', 'rheumatoid', 'arthritis', 'lupus', 'there', 'reliable', 'evidence', 'prevent', 'treat', 'covid', 'some', 'studies', 'found', 'make', 'things', 'worse', 'remdesivir', 'antiviral', 'medicine', 'that', 'help', 'some', 'people', 'hospital', 'with', 'severe', 'covid', 'recover', 'faster', 'does', 'help', 'people', 'with', 'milder', 'covid', 'dexamethasone', 'reduces', 'inflammation', 'used', 'treat', 'asthma', 'arthritis', 'reduces', 'risk', 'dying', 'people', 'hospital', 'with', 'serious', 'covid', 'that', 'need', 'help', 'breathe', 'doesn', 'help', 'people', 'with', 'less', 'severe', 'covid', 'could', 'make', 'things', 'worse', 'ephedra', 'vitamin', 'other', 'complementary', 'medicines', 'none', 'found', 'effective', 'against', 'covid', 'outside', 'intensive', 'care', 'source', 'medicinewisenew', 'newspaper']
['covid', 'schools', 'study', 'breakdownby', 'australian', 'associated', 'presspublished', 'april', '2020', 'updated', 'april', '2020what', 'national', 'centre', 'immunisation', 'research', 'surveillance', 'ncirs', 'study', 'into', 'covid', 'schools', 'found', 'total', 'students', 'staff', 'members', 'from', 'schools', 'were', 'from', 'march', 'april', '2020', 'diagnosed', 'with', 'covid', 'people', 'nine', 'were', 'students', 'nine', 'were', 'staff', 'members', 'those', 'people', 'total', 'close', 'contacts', 'within', 'school', 'over', 'that', 'time', 'period', 'close', 'contacts', 'just', 'people', 'caught', 'covid', 'those', 'people', 'both', 'students', 'pass', 'disease', 'others', 'students', 'passed', 'coronavirus', 'school', 'staff', 'members', 'spread', 'covid', 'within', 'schools', 'thus', 'been', 'very', 'limited', 'virus', 'transmission', 'children', 'schools', 'less', 'than', 'that', 'influenza', 'newspaper']
['sydney', 'club', 'shut', 'repeat', 'covid', 'breachesby', 'australian', 'associated', 'presspublished', 'october', '2020', 'updated', 'october', '2020the', 'sydney', 'portugal', 'community', 'club', 'being', 'forced', 'shut', 'seven', 'days', 'after', 'failing', 'comply', 'with', 'covid', 'safe', 'regulations', 'even', 'after', 'three', 'warnings', '5000', 'fine', 'liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'said', 'portugal', 'club', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches', 'will', 'close', 'from', 'wednesday', 'week', 'inspectors', 'visited', 'club', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'plan', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'said', 'tuesday', 'since', 'closure', 'order', 'issued', 'however', 'some', 'things', 'club', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'than', 'while', 'guests', 'need', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines', 'what', 'changed', 'having', 'robust', 'digital', 'check', 'processes', 'that', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'case', 'visited', 'venue', 'argeres', 'said', 'service', 'free', 'covid', 'safe', 'check', 'code', 'businesses', 'registered', 'covid', 'safe', 'anyone', 'check', 'using', 'this', 'code', 'even', 'they', 'have', 'service', 'account', 'inspectors', 'from', 'liquor', 'gaming', 'fair', 'trading', 'safework', 'have', 'conducted', '5605', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'four', 'businesses', 'newspaper']
['tests', 'underway', 'covid', 'treatmentby', 'australian', 'associated', 'presspublished', 'august', '2020', 'updated', 'august', '2020australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'they', 'hope', 'will', 'provide', 'relief', 'those', 'infected', 'with', 'covid', 'limit', 'spread', 'professor', 'david', 'morris', 'international', 'team', 'have', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'that', 'could', 'prevent', 'virus', 'moving', 'into', 'lungs', 'carriers', 'spreading', 'other', 'people', 'trial', 'coronavirus', 'patients', 'being', 'established', 'with', 'melbourne', 'hospital', 'could', 'start', 'next', 'month', 'bromac', 'been', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'that', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells', 'core', 'agents', 'pineapple', 'stem', 'enzyme', 'tested', 'after', 'observed', 'that', 'pigs', 'eating', 'pineapples', 'were', 'resistant', 'particular', 'gastro', 'conditions', 'taken', 'drug', 'development', 'more', 'than', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'with', 'covid', 'professor', 'david', 'morris', 'said', 'statement', 'monday', 'results', 'show', 'drug', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'from', 'infecting', 'other', 'cells', 'hope', 'results', 'will', 'show', 'treatment', 'confine', 'covid', 'nose', 'throat', 'prevent', 'lung', 'infection', 'stop', 'infected', 'patients', 'from', 'passing', 'live', 'virus', 'said', 'hoped', 'infected', 'person', 'could', 'take', 'medication', 'first', 'signs', 'covid', 'stop', 'virus', 'tracks', 'this', 'could', 'provide', 'very', 'safe', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'back', 'their', 'everyday', 'lives', 'researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'work', 'tandem', 'treatment', 'prevention', 'measure']

# LSA Model
number_of_topics=20
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))#dict and encoding matrix values

#dictionary of top 5 words
dict(model_nscience.show_topic(0,topn=5))

{'test': 0.5425296037655409,
 'covid': 0.30373216046545803,
 'peopl': 0.2994432685604926,
 'symptom': 0.18363931537689843,
 'case': 0.18310800492471946}

#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph7.png')

# cosine similarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled7.png')

cosine-similarity
              test      covid      peopl    symptom       case
test             1  0.0622567   0.326012   0.864501    0.32399
covid    0.0622567          1   0.313763 -0.0833804   0.190355
peopl     0.326012   0.313763          1   0.446673 -0.0764638
symptom   0.864501 -0.0833804   0.446673          1   0.284991
case       0.32399   0.190355 -0.0764638   0.284991          1

#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="y")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig7.png')

lsa on covid-psychology.txt at article level

#data loading
document_list,titles,word_count_psy=load_data("","covid-psychology.txt",'NEW PAPER')

Number of words in text file : 86550
Total Number of Documents: 20

#data preprocessing
clean_text=preprocess_data(document_list)

['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '1177', '0268580920948807journals', 'sagepub', 'home', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'thisreview', 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'psychological', 'social', 'behavioral', 'dimensions', 'cancer', '2007', 'shen', 'rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'from', 'linguistic', 'signal', 'clinical', 'reality', 'pages', '2017', 'shultz', 'baingana', 'neria', '2014', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response', 'jama', '2015', 'simms', 'ramstedt', 'rich', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortionsthrough', 'machine', 'learning', 'text', 'analytics', '2017', 'ieee', 'international', 'conference', 'healthcare', 'informatics', 'ichi', 'pages', 'ieee', '2017', 'smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association', '2012', 'sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'fake', 'news', 'exploringhealth', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'case', 'study', 'american', 'journalof', 'health', 'education', '2018', 'strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness', '1990', 'tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizationalidentity', '1979', 'tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'liwc', 'computerized', 'text', 'analysismethods', 'journal', 'language', 'social', 'psychology', '2010', 'epidemic', 'psychology', 'works', 'twitter', '21the', 'economist', 'with', 'millions', 'stuck', 'home', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international', '2020', 'with', 'millions', 'stuck', 'home', 'online', 'wellness', 'industry', 'booming', 'bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett', 'crum', 'douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response', 'nature', 'human', 'behaviour', 'pages', '2020', 'varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection', 'estimation', 'characterization', 'eleventh', 'international', 'aaai', 'conference', 'social', 'media', '2017', 'wang', 'immediate', 'psychological', 'responses', 'associatedfactors', 'during', 'initial', 'stage', '2019', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'populationin', 'china', 'international', 'journal', 'environmental', 'research', 'public', 'health', '1729', '2020', 'waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion', 'comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society', '1813', '1831', '2018', 'windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises', 'risk', 'hazards', 'crisis', 'public', 'policy', '2014', 'wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center', '2019', 'wolf', 'theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'onlineactivity', 'journal', 'language', 'social', 'psychology', '2013', 'wood', 'propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', '2015', '2016', 'zika', 'virus', 'outbreak', 'cyberpsychology', 'behavior', 'social', 'networking', '2018', 'yang', 'torres', 'lugo', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during', 'thecovid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '2004', '14484', '2020', 'acknowledgmentswe', 'thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'this', 'researchwas', 'partly', 'supported', 'grant', 'green', 'routes', '869764', 'data', 'availabilitythe', 'daily', 'aggregates', 'measurements', 'available', 'https', '6084', 'figshare', '14892642', 'tweet', 'used', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasetsand', 'visualizations', 'available', 'project', 'site', 'http', 'social', 'dynamics', 'epidemicpsychology', 'competing', 'intereststhe', 'author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twittersupplementary', 'materialsspatial', 'representativeness', 'twitter', 'data', 'make', 'sure', 'that', 'geographical', 'penetration', 'twitterdata', 'reflects', 'spatial', 'distribution', 'population', 'compared', 'user', 'activity', 'each', 'state', 'withthe', 'census', 'population', 'figure', 'shows', 'that', 'both', 'number', 'twitter', 'users', 'volume', 'their', 'tweetscorrelate', 'very', 'strongly', 'with', 'census', 'population', 'population', 'estimates', 'year', '2019', 'level', 'states', 'https', 'census', '17log', 'population', '1213141516log', 'tweets', '939r', '08814', '17log', 'population', '91011121314log', 'users', '952r', '106figure', 'correlation', 'census', 'population', 'states', 'estimated', 'year', '2019', 'with', 'thenumber', 'tweets', 'posted', 'those', 'states', 'left', 'number', 'unique', 'twitter', 'users', 'postedat', 'least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together', 'with', 'thecoefficient', 'determination', 'linear', 'regression', 'pearson', 'correlation', 'coefficient', 'andthe', 'coefficient', 'number', 'tweets', 'that', 'twitter', 'users', 'scale', 'almost', 'linearlywith', 'population', 'estimates', 'signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'that', 'first', 'phase', 'wascharacterized', 'discussions', 'that', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones', 'wemeasured', 'volume', 'tweets', 'containing', 'hashtags', 'that', 'were', 'clear', 'indicators', 'content', 'that', 'either', 'racist', 'oraimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'from', 'list', 'hashtags', 'that', 'appeared', 'atleast', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags', 'over', 'time', 'theypeak', 'during', 'first', 'phase', 'when', 'fear', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asianforeigners', 'were', 'their', 'peaks', 'breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'into', 'regions', 'thatwere', 'dealing', 'with', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assesswhether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed', 'themeasures', 'that', 'represent', 'relative', 'presence', 'three', 'phases', 'over', 'time', 'refusal', 'anger', 'acceptance', 'each', 'state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curvesindicate', 'that', 'three', 'phases', 'took', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states', 'refusal', 'followed', 'anger', 'which', 'then', 'followed', 'acceptance', 'there', 'very', 'little', 'variation', 'thetimes', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']

# LSA Model
number_of_topics=20
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_articles=create_gensim_lsa_model(clean_text,number_of_topics,words)

#top 5 words from lsa
dict(model_psy.show_topic(0,topn=5))

{'2020': 0.42727157511952374,
 'covid': 0.32099481957500275,
 'psycholog': 0.22665575223350956,
 'social': 0.21129956668978256,
 'epidem': 0.16257116253243872}

#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph27.png')

# cosine similarity matrix for top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled8.png')

cosine-similarity
                2020      covid psycholog     social     epidem
2020               1   0.282638  0.146216   0.086535  0.0550711
covid       0.282638          1  0.137066  0.0146927 -0.0702621
psycholog   0.146216   0.137066         1   0.116781   0.101001
social      0.086535  0.0146927  0.116781          1   0.434874
epidem     0.0550711 -0.0702621  0.101001   0.434874          1

#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig8.png')

# defining mantel test function
def mantel_test(n):
    #all words from corpus
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))
    #top words from the corpus
    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_psy = dict(model_psy.show_topic(0,topn=n)) 

    #Finding out the weights of top 5 law words in covid science and updating their weights w.r.t psychology dataset 
    c={} 
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)
    #Finding out the weights of top 5 law words in covid psychology and updating their weights w.r.t covid science dataset 
    b={} 
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for top 10 words in covid science
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace of top 10 words in psychology dataset
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')

mantel_test(5)

Pairwise-distance
               2020     covid    psycholog    social       epidem  \
2020              0  0.674002      0.36767  0.531836     0.396413   
covid      0.674002         0     0.629265  0.734645     0.650745   
psycholog   0.36767  0.629265  8.23181e-11  0.400991     0.102067   
social     0.531836  0.734645     0.400991         0     0.418365   
epidem     0.396413  0.650745     0.102067  0.418365  1.86265e-09   
imag       0.891661  0.906757     0.790683  0.916729     0.795824   
data       0.857554  0.768464     0.722907  0.832332     0.723699   
use        0.555855  0.657712     0.474182  0.666313     0.488571   
model      0.711022  0.902144      0.68823  0.806082     0.669871   

                  imag         data          use     model  
2020          0.891661     0.857554     0.555855  0.711022  
covid         0.906757     0.768464     0.657712  0.902144  
psycholog     0.790683     0.722907     0.474182   0.68823  
social        0.916729     0.832332     0.666313  0.806082  
epidem        0.795824     0.723699     0.488571  0.669871  
imag       1.49012e-08      1.13106     0.889194   1.09935  
data           1.13106  1.49012e-08     0.873157  0.987321  
use           0.889194     0.873157  1.05367e-08  0.810734  
model          1.09935     0.987321     0.810734         0  
Pairwise-distance
               2020     covid    psycholog    social    epidem      imag  \
2020              0  0.735209     0.749397  0.760129  0.753629   0.65016   
covid      0.735209         0     0.701633  0.732958  0.740581  0.569647   
psycholog  0.749397  0.701633  1.05367e-08  0.628189  0.611607  0.494348   
social     0.760129  0.732958     0.628189         0  0.470988  0.446562   
epidem     0.753629  0.740581     0.611607  0.470988         0  0.430313   
imag        0.65016  0.569647     0.494348  0.446562  0.430313         0   
data       0.699952  0.503389     0.515389   0.49647  0.436326  0.203807   
use        0.691721  0.588164     0.439474  0.445464  0.388983  0.249131   
model      0.621761  0.572642     0.523753  0.420748  0.366641  0.169949   

                  data       use     model  
2020          0.699952  0.691721  0.621761  
covid         0.503389  0.588164  0.572642  
psycholog     0.515389  0.439474  0.523753  
social         0.49647  0.445464  0.420748  
epidem        0.436326  0.388983  0.366641  
imag          0.203807  0.249131  0.169949  
data       3.72529e-09  0.221334  0.241608  
use           0.221334         0  0.191126  
model         0.241608  0.191126         0  

(-0.533093121849338, 0.95076, -1.6363094990614924)

#mantel test with 20 word network

mantel_test(10)

Pairwise-distance
               2020     covid    psycholog    social       epidem    pandem  \
2020              0  0.674002      0.36767  0.531836     0.396413  0.418207   
covid      0.674002         0     0.629265  0.734645     0.650745  0.647843   
psycholog   0.36767  0.629265  8.23181e-11  0.400991     0.102067  0.172896   
social     0.531836  0.734645     0.400991         0     0.418365  0.368846   
epidem     0.396413  0.650745     0.102067  0.418365  1.86265e-09  0.132024   
pandem     0.418207  0.647843     0.172896  0.368846     0.132024         0   
health     0.371036  0.615536     0.172764  0.403539     0.189056  0.204128   
time       0.490966  0.687675     0.350242  0.493539     0.309363  0.378335   
group       0.37055  0.613001    0.0774391  0.394031     0.133837  0.188081   
peopl      0.348986  0.664598     0.258756   0.34618     0.284587  0.278945   
imag       0.891661  0.906757     0.790683  0.916729     0.795824  0.816846   
data       0.857554  0.768464     0.722907  0.832332     0.723699  0.718704   
use        0.555855  0.657712     0.474182  0.666313     0.488571  0.509923   
model      0.711022  0.902144      0.68823  0.806082     0.669871  0.687796   
dataset    0.715219  0.890297     0.573152  0.662778     0.603267  0.612139   
class      0.569307   0.64165      0.48671  0.658691     0.507188  0.543696   
learn      0.650257  0.749877     0.538634  0.701563     0.540577  0.547968   
perform     0.45835  0.657498     0.343949  0.560799       0.3769  0.392621   
train      0.458609  0.593987     0.325781  0.528955     0.330647  0.333289   

             health      time      group     peopl         imag         data  \
2020       0.371036  0.490966    0.37055  0.348986     0.891661     0.857554   
covid      0.615536  0.687675   0.613001  0.664598     0.906757     0.768464   
psycholog  0.172764  0.350242  0.0774391  0.258756     0.790683     0.722907   
social     0.403539  0.493539   0.394031   0.34618     0.916729     0.832332   
epidem     0.189056  0.309363   0.133837  0.284587     0.795824     0.723699   
pandem     0.204128  0.378335   0.188081  0.278945     0.816846     0.718704   
health            0  0.379101   0.177002  0.308461     0.823753     0.741774   
time       0.379101         0   0.335529  0.413296     0.864039     0.747668   
group      0.177002  0.335529          0  0.267843     0.784342     0.728073   
peopl      0.308461  0.413296   0.267843         0     0.854849     0.778094   
imag       0.823753  0.864039   0.784342  0.854849  1.49012e-08      1.13106   
data       0.741774  0.747668   0.728073  0.778094      1.13106  1.49012e-08   
use         0.49672  0.544303    0.45891  0.532211     0.889194     0.873157   
model      0.696478  0.702871   0.710612  0.748253      1.09935     0.987321   
dataset    0.601041  0.716844   0.597873  0.630252     0.884121     0.853085   
class      0.519957  0.654512   0.522106  0.564029      0.91668     0.910914   
learn      0.539503  0.670617   0.532541  0.625019       0.9801     0.956935   
perform    0.399797  0.551318   0.353306  0.420931     0.844045     0.787457   
train      0.371207  0.490915   0.337769  0.413696     0.709201     0.804153   

                   use     model   dataset     class     learn      perform  \
2020          0.555855  0.711022  0.715219  0.569307  0.650257      0.45835   
covid         0.657712  0.902144  0.890297   0.64165  0.749877     0.657498   
psycholog     0.474182   0.68823  0.573152   0.48671  0.538634     0.343949   
social        0.666313  0.806082  0.662778  0.658691  0.701563     0.560799   
epidem        0.488571  0.669871  0.603267  0.507188  0.540577       0.3769   
pandem        0.509923  0.687796  0.612139  0.543696  0.547968     0.392621   
health         0.49672  0.696478  0.601041  0.519957  0.539503     0.399797   
time          0.544303  0.702871  0.716844  0.654512  0.670617     0.551318   
group          0.45891  0.710612  0.597873  0.522106  0.532541     0.353306   
peopl         0.532211  0.748253  0.630252  0.564029  0.625019     0.420931   
imag          0.889194   1.09935  0.884121   0.91668    0.9801     0.844045   
data          0.873157  0.987321  0.853085  0.910914  0.956935     0.787457   
use        1.05367e-08  0.810734  0.697269  0.660308  0.660297     0.473797   
model         0.810734         0  0.895999  0.816373  0.860437     0.567306   
dataset       0.697269  0.895999         0  0.715456  0.822228     0.673615   
class         0.660308  0.816373  0.715456         0  0.698663     0.631055   
learn         0.660297  0.860437  0.822228  0.698663         0     0.599322   
perform       0.473797  0.567306  0.673615  0.631055  0.599322  5.26836e-09   
train         0.615921   0.69883  0.684229  0.564319  0.578432      0.40463   

              train  
2020       0.458609  
covid      0.593987  
psycholog  0.325781  
social     0.528955  
epidem     0.330647  
pandem     0.333289  
health     0.371207  
time       0.490915  
group      0.337769  
peopl      0.413696  
imag       0.709201  
data       0.804153  
use        0.615921  
model       0.69883  
dataset    0.684229  
class      0.564319  
learn      0.578432  
perform     0.40463  
train             0  
Pairwise-distance
               2020     covid    psycholog    social    epidem       pandem  \
2020              0  0.735209     0.749397  0.760129  0.753629     0.541543   
covid      0.735209         0     0.701633  0.732958  0.740581     0.557089   
psycholog  0.749397  0.701633  1.05367e-08  0.628189  0.611607     0.527426   
social     0.760129  0.732958     0.628189         0  0.470988     0.592742   
epidem     0.753629  0.740581     0.611607  0.470988         0     0.600247   
pandem     0.541543  0.557089     0.527426  0.592742  0.600247  7.45058e-09   
health     0.731581  0.861669     0.631573  0.749823   0.75502     0.630955   
time       0.615462  0.632654     0.511761  0.410078  0.440071     0.474871   
group        1.0016  0.936565      0.91457  0.798941  0.906831     0.860574   
peopl       0.83341  0.739808     0.686906  0.539341  0.690625     0.558758   
imag        0.65016  0.569647     0.494348  0.446562  0.430313     0.359011   
data       0.699952  0.503389     0.515389   0.49647  0.436326     0.368065   
use        0.691721  0.588164     0.439474  0.445464  0.388983     0.428385   
model      0.621761  0.572642     0.523753  0.420748  0.366641     0.408663   
dataset     0.64451  0.578329     0.486274  0.457292  0.419513     0.367752   
class      0.649637  0.582104     0.485732   0.46168  0.419733     0.373695   
learn      0.602956  0.581803     0.504455  0.467341  0.418748     0.360697   
perform    0.686484  0.613461     0.524585   0.50715  0.470766     0.396873   
train      0.629644  0.580622     0.494149   0.49852  0.445494     0.375578   

             health         time     group        peopl       imag  \
2020       0.731581     0.615462    1.0016      0.83341    0.65016   
covid      0.861669     0.632654  0.936565     0.739808   0.569647   
psycholog  0.631573     0.511761   0.91457     0.686906   0.494348   
social     0.749823     0.410078  0.798941     0.539341   0.446562   
epidem      0.75502     0.440071  0.906831     0.690625   0.430313   
pandem     0.630955     0.474871  0.860574     0.558758   0.359011   
health            0     0.668585   1.00445     0.843911   0.625065   
time       0.668585  5.26836e-09  0.802166     0.536735   0.266881   
group       1.00445     0.802166         0     0.788851   0.767416   
peopl      0.843911     0.536735  0.788851  7.45058e-09   0.495739   
imag       0.625065     0.266881  0.767416     0.495739          0   
data       0.682985     0.347752  0.788488      0.57412   0.203807   
use        0.705662     0.342372  0.813728     0.582681   0.249131   
model        0.6665     0.254812  0.795447      0.53675   0.169949   
dataset    0.625121     0.264936  0.762475     0.499407  0.0400685   
class      0.622407     0.267353  0.760009     0.489682   0.052582   
learn      0.646428     0.282943  0.769878     0.507473   0.102833   
perform    0.667897     0.293216  0.793514     0.550034   0.202954   
train      0.643295     0.288389  0.785966     0.516929   0.134089   

                  data       use     model    dataset        class  \
2020          0.699952  0.691721  0.621761    0.64451     0.649637   
covid         0.503389  0.588164  0.572642   0.578329     0.582104   
psycholog     0.515389  0.439474  0.523753   0.486274     0.485732   
social         0.49647  0.445464  0.420748   0.457292      0.46168   
epidem        0.436326  0.388983  0.366641   0.419513     0.419733   
pandem        0.368065  0.428385  0.408663   0.367752     0.373695   
health        0.682985  0.705662    0.6665   0.625121     0.622407   
time          0.347752  0.342372  0.254812   0.264936     0.267353   
group         0.788488  0.813728  0.795447   0.762475     0.760009   
peopl          0.57412  0.582681   0.53675   0.499407     0.489682   
imag          0.203807  0.249131  0.169949  0.0400685     0.052582   
data       3.72529e-09  0.221334  0.241608   0.212732     0.220608   
use           0.221334         0  0.191126   0.236281     0.242491   
model         0.241608  0.191126         0   0.151736     0.164574   
dataset       0.212732  0.236281  0.151736          0    0.0293471   
class         0.220608  0.242491  0.164574  0.0293471  4.65661e-10   
learn         0.231899  0.250769  0.177662  0.0908676    0.0919756   
perform       0.279963  0.317854  0.257718    0.20024      0.20535   
train         0.246193  0.279958  0.211779    0.12333      0.12912   

                 learn   perform     train  
2020          0.602956  0.686484  0.629644  
covid         0.581803  0.613461  0.580622  
psycholog     0.504455  0.524585  0.494149  
social        0.467341   0.50715   0.49852  
epidem        0.418748  0.470766  0.445494  
pandem        0.360697  0.396873  0.375578  
health        0.646428  0.667897  0.643295  
time          0.282943  0.293216  0.288389  
group         0.769878  0.793514  0.785966  
peopl         0.507473  0.550034  0.516929  
imag          0.102833  0.202954  0.134089  
data          0.231899  0.279963  0.246193  
use           0.250769  0.317854  0.279958  
model         0.177662  0.257718  0.211779  
dataset      0.0908676   0.20024   0.12333  
class        0.0919756   0.20535   0.12912  
learn      1.86265e-09  0.213694  0.103606  
perform       0.213694         0  0.162208  
train         0.103606  0.162208         0  

(-0.513948348056973, 0.99636, -2.35975177848905)

# mantel test for covid science and non science dataset(Not necessary for experimental results of project)

def mantel_test_n(n):
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_nscience = dict(model_nscience.show_topic(0, topn=word_count_nscience))

    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_nscience = dict(model_nscience.show_topic(0,topn=n)) 


    c={} #law words in non law
    for key in top_words_science:
        try:
            c[key] = words_nscience[key]
        except KeyError:
            c[key] = 0

    top_words_nscience.update(c)

    b={} 
    for key in top_words_nscience:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance 
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace for non-law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_nscience =dict(model_nscience.show_topic(i, topn=word_count_nscience))
        b=pd.DataFrame(words_from_nscience,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_nscience.keys()), 
                      index = list(top_words_nscience.keys()))

    l = list(top_words_nscience.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_nscience), k = 1)])

    return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')

mantel_test_n(5)

Pairwise-distance
                test     covid     peopl   symptom      case         imag  \
test     1.05367e-08  0.750979  0.491932  0.533855  0.798228     0.987696   
covid       0.750979         0  0.664598  0.621303  0.822785     0.906757   
peopl       0.491932  0.664598         0  0.270493  0.579838     0.854849   
symptom     0.533855  0.621303  0.270493         0  0.518144     0.792381   
case        0.798228  0.822785  0.579838  0.518144         0     0.940637   
imag        0.987696  0.906757  0.854849  0.792381  0.940637  1.49012e-08   
data        0.893981  0.768464  0.778094  0.726166  0.804397      1.13106   
use         0.665969  0.657712  0.532211  0.457572  0.667066     0.889194   
model       0.860115  0.902144  0.748253  0.692453  0.791349      1.09935   

                data          use     model  
test        0.893981     0.665969  0.860115  
covid       0.768464     0.657712  0.902144  
peopl       0.778094     0.532211  0.748253  
symptom     0.726166     0.457572  0.692453  
case        0.804397     0.667066  0.791349  
imag         1.13106     0.889194   1.09935  
data     1.49012e-08     0.873157  0.987321  
use         0.873157  1.05367e-08  0.810734  
model       0.987321     0.810734         0  
Pairwise-distance
             test        covid     peopl      symptom      case imag  \
test            0     0.960096  0.707701     0.474889  0.670867    0   
covid    0.960096  1.49012e-08   0.74718     0.786907   0.76285    0   
peopl    0.707701      0.74718         0     0.466821  0.691487    0   
symptom  0.474889     0.786907  0.466821  5.26836e-09  0.421852    0   
case     0.670867      0.76285  0.691487     0.421852         0    0   
imag            0            0         0            0         0    0   
data     0.745108     0.755606  0.594656     0.360268  0.401465    0   
use      0.742916     0.665757  0.515411     0.330993   0.45149    0   
model    0.681393     0.724479  0.522393     0.256259  0.420446    0   

                data          use        model  
test        0.745108     0.742916     0.681393  
covid       0.755606     0.665757     0.724479  
peopl       0.594656     0.515411     0.522393  
symptom     0.360268     0.330993     0.256259  
case        0.401465      0.45149     0.420446  
imag               0            0            0  
data     3.72529e-09      0.26882     0.252962  
use          0.26882  3.72529e-09     0.202653  
model       0.252962     0.202653  6.58545e-10  

(-0.45038456855907627, 0.90518, -1.3821255209451901)

mantel_test_n(10)

Pairwise-distance
                    test     covid     peopl    symptom      case     travel  \
test         1.05367e-08  0.750979  0.491932   0.533855  0.798228   0.533196   
covid           0.750979         0  0.664598   0.621303  0.822785    0.62861   
peopl           0.491932  0.664598         0   0.270493  0.579838   0.259909   
symptom         0.533855  0.621303  0.270493          0  0.518144  0.0647461   
case            0.798228  0.822785  0.579838   0.518144         0   0.529687   
travel          0.533196   0.62861  0.259909  0.0647461  0.529687          0   
fever           0.528434  0.619159  0.266031  0.0488287  0.534801  0.0389511   
infect          0.642507  0.715799  0.495376   0.413898  0.581321   0.411621   
contact         0.493119  0.628239  0.235357  0.0944009  0.533702   0.100415   
respiratori      0.53334  0.624215  0.266039  0.0607611  0.519514  0.0466501   
imag            0.987696  0.906757  0.854849   0.792381  0.940637   0.792253   
data            0.893981  0.768464  0.778094   0.726166  0.804397    0.71877   
use             0.665969  0.657712  0.532211   0.457572  0.667066   0.473015   
model           0.860115  0.902144  0.748253   0.692453  0.791349   0.687141   
dataset         0.751918  0.890297  0.630252   0.566885  0.699982   0.573304   
class           0.709419   0.64165  0.564029   0.505606  0.715628    0.48807   
learn           0.782049  0.749877  0.625019   0.552334  0.763608   0.530465   
perform         0.619943  0.657498  0.420931    0.34055  0.644937   0.343634   
train            0.64225  0.593987  0.413696   0.336186  0.607643   0.323423   

                 fever       infect    contact  respiratori         imag  \
test          0.528434     0.642507   0.493119      0.53334     0.987696   
covid         0.619159     0.715799   0.628239     0.624215     0.906757   
peopl         0.266031     0.495376   0.235357     0.266039     0.854849   
symptom      0.0488287     0.413898  0.0944009    0.0607611     0.792381   
case          0.534801     0.581321   0.533702     0.519514     0.940637   
travel       0.0389511     0.411621   0.100415    0.0466501     0.792253   
fever                0     0.406866  0.0966737    0.0446772     0.795248   
infect        0.406866  7.45058e-09   0.418462     0.416387     0.917136   
contact      0.0966737     0.418462          0     0.101398     0.805435   
respiratori  0.0446772     0.416387   0.101398  6.58545e-10     0.785179   
imag          0.795248     0.917136   0.805435     0.785179  1.49012e-08   
data           0.72522     0.845112   0.730394     0.728012      1.13106   
use           0.455654      0.69464   0.485141      0.45702     0.889194   
model         0.696961     0.748659   0.699363     0.682944      1.09935   
dataset       0.578419     0.782891    0.58464     0.577963     0.884121   
class          0.49579      0.66437   0.507371     0.490767      0.91668   
learn         0.539896     0.604111   0.567556      0.54391       0.9801   
perform       0.338136     0.582515   0.362636     0.324424     0.844045   
train         0.328509     0.482453    0.34788     0.316082     0.709201   

                    data          use     model   dataset     class     learn  \
test            0.893981     0.665969  0.860115  0.751918  0.709419  0.782049   
covid           0.768464     0.657712  0.902144  0.890297   0.64165  0.749877   
peopl           0.778094     0.532211  0.748253  0.630252  0.564029  0.625019   
symptom         0.726166     0.457572  0.692453  0.566885  0.505606  0.552334   
case            0.804397     0.667066  0.791349  0.699982  0.715628  0.763608   
travel           0.71877     0.473015  0.687141  0.573304   0.48807  0.530465   
fever            0.72522     0.455654  0.696961  0.578419   0.49579  0.539896   
infect          0.845112      0.69464  0.748659  0.782891   0.66437  0.604111   
contact         0.730394     0.485141  0.699363   0.58464  0.507371  0.567556   
respiratori     0.728012      0.45702  0.682944  0.577963  0.490767   0.54391   
imag             1.13106     0.889194   1.09935  0.884121   0.91668    0.9801   
data         1.49012e-08     0.873157  0.987321  0.853085  0.910914  0.956935   
use             0.873157  1.05367e-08  0.810734  0.697269  0.660308  0.660297   
model           0.987321     0.810734         0  0.895999  0.816373  0.860437   
dataset         0.853085     0.697269  0.895999         0  0.715456  0.822228   
class           0.910914     0.660308  0.816373  0.715456         0  0.698663   
learn           0.956935     0.660297  0.860437  0.822228  0.698663         0   
perform         0.787457     0.473797  0.567306  0.673615  0.631055  0.599322   
train           0.804153     0.615921   0.69883  0.684229  0.564319  0.578432   

                 perform     train  
test            0.619943   0.64225  
covid           0.657498  0.593987  
peopl           0.420931  0.413696  
symptom          0.34055  0.336186  
case            0.644937  0.607643  
travel          0.343634  0.323423  
fever           0.338136  0.328509  
infect          0.582515  0.482453  
contact         0.362636   0.34788  
respiratori     0.324424  0.316082  
imag            0.844045  0.709201  
data            0.787457  0.804153  
use             0.473797  0.615921  
model           0.567306   0.69883  
dataset         0.673615  0.684229  
class           0.631055  0.564319  
learn           0.599322  0.578432  
perform      5.26836e-09   0.40463  
train            0.40463         0  
Pairwise-distance
                 test        covid     peopl      symptom      case    travel  \
test                0     0.960096  0.707701     0.474889  0.670867  0.533249   
covid        0.960096  1.49012e-08   0.74718     0.786907   0.76285  0.780415   
peopl        0.707701      0.74718         0     0.466821  0.691487  0.499547   
symptom      0.474889     0.786907  0.466821  5.26836e-09  0.421852    0.1642   
case         0.670867      0.76285  0.691487     0.421852         0  0.455464   
travel       0.533249     0.780415  0.499547       0.1642  0.455464         0   
fever        0.516369     0.767072  0.466238    0.0602064  0.423661   0.14079   
infect       0.777339     0.815879  0.713788     0.553901  0.647398  0.522268   
contact       0.59684     0.716573  0.501878     0.275565  0.452727  0.306657   
respiratori  0.529887      0.76097  0.467876    0.0763009  0.418966  0.137889   
imag                0            0         0            0         0         0   
data         0.745108     0.755606  0.594656     0.360268  0.401465  0.343604   
use          0.742916     0.665757  0.515411     0.330993   0.45149   0.29489   
model        0.681393     0.724479  0.522393     0.256259  0.420446  0.222238   
dataset             0            0         0            0         0         0   
class        0.681496     0.726887  0.505246     0.260397    0.4196  0.227429   
learn        0.683699     0.717042  0.532415     0.262241  0.416605  0.234838   
perform             0            0         0            0         0         0   
train        0.680553     0.722203  0.524164     0.256927  0.417977  0.222096   

                 fever    infect   contact respiratori imag         data  \
test          0.516369  0.777339   0.59684    0.529887    0     0.745108   
covid         0.767072  0.815879  0.716573     0.76097    0     0.755606   
peopl         0.466238  0.713788  0.501878    0.467876    0     0.594656   
symptom      0.0602064  0.553901  0.275565   0.0763009    0     0.360268   
case          0.423661  0.647398  0.452727    0.418966    0     0.401465   
travel         0.14079  0.522268  0.306657    0.137889    0     0.343604   
fever                0  0.533792  0.251857   0.0184378    0     0.323235   
infect        0.533792         0  0.603863    0.526883    0     0.564586   
contact       0.251857  0.603863         0     0.24682    0     0.338266   
respiratori  0.0184378  0.526883   0.24682           0    0     0.311659   
imag                 0         0         0           0    0            0   
data          0.323235  0.564586  0.338266    0.311659    0  3.72529e-09   
use           0.289428  0.571065  0.345758    0.276271    0      0.26882   
model         0.204474  0.491545  0.268131    0.186408    0     0.252962   
dataset              0         0         0           0    0            0   
class         0.208538  0.494189  0.275373    0.190402    0     0.251244   
learn         0.212028  0.500498  0.291114    0.194696    0     0.260372   
perform              0         0         0           0    0            0   
train         0.204653  0.494128  0.269468    0.186544    0     0.252847   

                     use        model dataset      class      learn perform  \
test            0.742916     0.681393       0   0.681496   0.683699       0   
covid           0.665757     0.724479       0   0.726887   0.717042       0   
peopl           0.515411     0.522393       0   0.505246   0.532415       0   
symptom         0.330993     0.256259       0   0.260397   0.262241       0   
case             0.45149     0.420446       0     0.4196   0.416605       0   
travel           0.29489     0.222238       0   0.227429   0.234838       0   
fever           0.289428     0.204474       0   0.208538   0.212028       0   
infect          0.571065     0.491545       0   0.494189   0.500498       0   
contact         0.345758     0.268131       0   0.275373   0.291114       0   
respiratori     0.276271     0.186408       0   0.190402   0.194696       0   
imag                   0            0       0          0          0       0   
data             0.26882     0.252962       0   0.251244   0.260372       0   
use          3.72529e-09     0.202653       0   0.205026   0.216197       0   
model           0.202653  6.58545e-10       0     0.0528  0.0795498       0   
dataset                0            0       0          0          0       0   
class           0.205026       0.0528       0          0  0.0802578       0   
learn           0.216197    0.0795498       0  0.0802578          0       0   
perform                0            0       0          0          0       0   
train           0.197632    0.0548807       0  0.0521263  0.0777509       0   

                 train  
test          0.680553  
covid         0.722203  
peopl         0.524164  
symptom       0.256927  
case          0.417977  
travel        0.222096  
fever         0.204653  
infect        0.494128  
contact       0.269468  
respiratori   0.186544  
imag                 0  
data          0.252847  
use           0.197632  
model        0.0548807  
dataset              0  
class        0.0521263  
learn        0.0777509  
perform              0  
train                0  

(-0.045145602008440484, 0.58879, -0.22155190658315219)

2. 2-3 paragraphs

LSA on covid-science.txt at 2-3 paragraph level

#loading dataset
def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for word in words:
                i=i+1
                document = document+word+' '
                if i == 200:
                    documents_list.append(document)
                    document,i = '',0
                    if len(documents_list) == 2:
                        break
    print("Total Number of Documents:",len(documents_list))
    #titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science.txt")

Number of words in text file : 120257
Total Number of Documents: 2

#data preprocessing
clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'most', 'medical', 'centers', 'hence', 'many', 'cases', 'images', 'become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks', 'have', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'with', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'such', 'systems', 'they', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'most', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levels', 'manuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date']
['publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '2020', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '2015', '18136', 'gómez', 'ríos', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'this', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'with', 'andalusian', 'research', 'institute', 'data', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'area', 'with', 'atlanttic', 'research', 'center']

# LSA Model
number_of_topics=2
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_p=create_gensim_lsa_model(clean_text,number_of_topics,words)

#top 5 words from LSA
dict(model_science.show_topic(0, topn=5))

{'support': -0.30386095534296476,
 '2020': -0.23522191481735744,
 'granada': -0.23033778261060142,
 'covid': -0.21722636684891058,
 'programm': -0.2025739702286432}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph8.png')

# Cosine similarity matrix

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled9.png')

cosine-similarity
           support      2020   granada     covid  programm
support          1  0.524928  0.955931 -0.115612         1
2020      0.524928         1  0.751685  0.784751  0.524928
granada   0.955931  0.751685         1  0.181107  0.955931
covid    -0.115612  0.784751  0.181107         1 -0.115612
programm         1  0.524928  0.955931 -0.115612         1

#networkx
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig9.png')

LSA on covid-non-science.txt at 2-3 paragraph level

#data loading
document_list,titles,word_count_nscience=load_data("","covid-non-science.txt")

Number of words in text file : 8713
Total Number of Documents: 2

#data cleaning
clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'reality', 'reuters', 'published', 'july', '2020', 'updated', 'july', '2020', 'marc', 'jones', 'london', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes', 'have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return']
['infection', 'rates', 'which', 'make', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june', 'euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases']

# LSA Model
number_of_topics=2
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values

#top 5 words
dict(model_nscience.show_topic(0, topn=5))

{'market': 0.264353040306379,
 'rate': 0.18754847840846847,
 'juli': 0.1791266417580859,
 'covid': 0.17912664175808563,
 'june': 0.17045279709658656}

#wordcloud of top 10 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph9.png')

# cosine similarity matrix for top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled10.png')

cosine-similarity
          market      rate      juli     covid      june
market         1  0.711061  0.771166  0.771166  0.459512
rate    0.711061         1   0.10071   0.10071  0.951241
juli    0.771166   0.10071         1         1  -0.21108
covid   0.771166   0.10071         1         1  -0.21108
june    0.459512  0.951241  -0.21108  -0.21108         1

#networkx
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig10.png')

LSA on covid-psychology.txt at 2-3 paragraph level

#data losding
document_list,titles,word_count_psy=load_data("","covid-psychology.txt")

Number of words in text file : 86550
Total Number of Documents: 2

#data preprocessing
clean_text=preprocess_data(document_list)

['international', 'sociology', '2021', 'author', '2020', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '1177', '0268580920948807', 'journals', 'sagepub', 'home', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid']
['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', '2020', 'covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated', 'data']

# LSA Model
number_of_topics=2
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_p = create_gensim_lsa_model(clean_text,number_of_topics,words)

#dictionary of top 5 words
dict(model_psy.show_topic(0, topn=5))

{'misinfodem': 0.4936266655941646,
 'covid': 0.3571916107745935,
 'articl': 0.21002057347124545,
 'mechan': 0.14717103730334843,
 'health': 0.1471710373033483}

#wordcloud
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph28.png')

# Cosinesimilarity matrix for top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled11.png')

cosine-similarity
           misinfodem     covid    articl    mechan    health
misinfodem          1  0.977431  0.994116  0.761706  0.761706
covid        0.977431         1  0.948797  0.881393  0.881393
articl       0.994116  0.948797         1  0.687043  0.687043
mechan       0.761706  0.881393  0.687043         1         1
health       0.761706  0.881393  0.687043         1         1

#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig11.png')

#mantel test for 10 word network
mantel_test(5)

Pairwise-distance
           misinfodem     covid    articl mechan health   support      2020  \
misinfodem          0         0         0      0      0         0         0   
covid               0         0  0.360337      0      0  0.608033  0.282616   
articl              0  0.360337         0      0      0  0.404283  0.208896   
mechan              0         0         0      0      0         0         0   
health              0         0         0      0      0         0         0   
support             0  0.608033  0.404283      0      0         0  0.327071   
2020                0  0.282616  0.208896      0      0  0.327071         0   
granada             0  0.457677  0.252649      0      0  0.161988  0.175514   
programm            0  0.526543  0.280864      0      0  0.126325  0.246469   

              granada   programm  
misinfodem          0          0  
covid        0.457677   0.526543  
articl       0.252649   0.280864  
mechan              0          0  
health              0          0  
support      0.161988   0.126325  
2020         0.175514   0.246469  
granada             0  0.0742376  
programm    0.0742376          0  
Pairwise-distance
           misinfodem     covid    articl     mechan     health support  \
misinfodem          0    0.1894  0.293937   0.419015   0.419015       0   
covid          0.1894         0  0.156809   0.234496   0.234496       0   
articl       0.293937  0.156809         0   0.170443   0.170443       0   
mechan       0.419015  0.234496  0.170443          0          0       0   
health       0.419015  0.234496  0.170443          0          0       0   
support             0         0         0          0          0       0   
2020         0.468991  0.293937    0.1894  0.0784044  0.0784044       0   
granada             0         0         0          0          0       0   
programm            0         0         0          0          0       0   

                 2020 granada programm  
misinfodem   0.468991       0        0  
covid        0.293937       0        0  
articl         0.1894       0        0  
mechan      0.0784044       0        0  
health      0.0784044       0        0  
support             0       0        0  
2020                0       0        0  
granada             0       0        0  
programm            0       0        0  

(-0.27891572318575647, 0.87877, -1.1456133484409994)

#mantel test for 20 word network
mantel_test(10)

Pairwise-distance
           misinfodem     covid    articl mechan health commun psycholog  \
misinfodem          0         0         0      0      0      0         0   
covid               0         0  0.360337      0      0      0         0   
articl              0  0.360337         0      0      0      0         0   
mechan              0         0         0      0      0      0         0   
health              0         0         0      0      0      0         0   
commun              0         0         0      0      0      0         0   
psycholog           0         0         0      0      0      0         0   
strategi            0         0         0      0      0      0         0   
misinform           0         0         0      0      0      0         0   
polit               0         0         0      0      0      0         0   
support             0  0.608033  0.404283      0      0      0         0   
2020                0  0.282616  0.208896      0      0      0         0   
granada             0  0.457677  0.252649      0      0      0         0   
programm            0  0.526543  0.280864      0      0      0         0   
research            0  0.526543  0.280864      0      0      0         0   
tabik               0  0.421418  0.189487      0      0      0         0   
garcía              0  0.318922  0.129222      0      0      0         0   
project             0  0.492938  0.220392      0      0      0         0   
decsai              0  0.492938  0.220392      0      0      0         0   

           strategi misinform polit   support       2020    granada  \
misinfodem        0         0     0         0          0          0   
covid             0         0     0  0.608033   0.282616   0.457677   
articl            0         0     0  0.404283   0.208896   0.252649   
mechan            0         0     0         0          0          0   
health            0         0     0         0          0          0   
commun            0         0     0         0          0          0   
psycholog         0         0     0         0          0          0   
strategi          0         0     0         0          0          0   
misinform         0         0     0         0          0          0   
polit             0         0     0         0          0          0   
support           0         0     0         0   0.327071   0.161988   
2020              0         0     0  0.327071          0   0.175514   
granada           0         0     0  0.161988   0.175514          0   
programm          0         0     0  0.126325   0.246469  0.0742376   
research          0         0     0  0.126325   0.246469  0.0742376   
tabik             0         0     0  0.220392   0.148475  0.0631623   
garcía            0         0     0  0.323975  0.0843519   0.161988   
project           0         0     0  0.189487   0.222713  0.0843519   
decsai            0         0     0  0.189487   0.222713  0.0843519   

             programm   research      tabik     garcía    project     decsai  
misinfodem          0          0          0          0          0          0  
covid        0.526543   0.526543   0.421418   0.318922   0.492938   0.492938  
articl       0.280864   0.280864   0.189487   0.129222   0.220392   0.220392  
mechan              0          0          0          0          0          0  
health              0          0          0          0          0          0  
commun              0          0          0          0          0          0  
psycholog           0          0          0          0          0          0  
strategi            0          0          0          0          0          0  
misinform           0          0          0          0          0          0  
polit               0          0          0          0          0          0  
support      0.126325   0.126325   0.220392   0.323975   0.189487   0.189487  
2020         0.246469   0.246469   0.148475  0.0843519   0.222713   0.222713  
granada     0.0742376  0.0742376  0.0631623   0.161988  0.0843519  0.0843519  
programm            0          0   0.109024   0.218047  0.0631623  0.0631623  
research            0          0   0.109024   0.218047  0.0631623  0.0631623  
tabik        0.109024   0.109024          0   0.109024  0.0742376  0.0742376  
garcía       0.218047   0.218047   0.109024          0   0.175514   0.175514  
project     0.0631623  0.0631623  0.0742376   0.175514          0          0  
decsai      0.0631623  0.0631623  0.0742376   0.175514          0          0  
Pairwise-distance
           misinfodem     covid     articl     mechan     health     commun  \
misinfodem          0    0.1894   0.293937   0.419015   0.419015   0.419015   
covid          0.1894         0   0.156809   0.234496   0.234496   0.234496   
articl       0.293937  0.156809          0   0.170443   0.170443   0.170443   
mechan       0.419015  0.234496   0.170443          0          0          0   
health       0.419015  0.234496   0.170443          0          0          0   
commun       0.419015  0.234496   0.170443          0          0          0   
psycholog    0.360699  0.235213  0.0784044   0.185806   0.185806   0.185806   
strategi     0.360699  0.235213  0.0784044   0.185806   0.185806   0.185806   
misinform    0.505066  0.315666   0.284614   0.115951   0.115951   0.115951   
polit        0.409223  0.240466   0.126266  0.0762243  0.0762243  0.0762243   
support             0         0          0          0          0          0   
2020         0.468991  0.293937     0.1894  0.0784044  0.0784044  0.0784044   
granada             0         0          0          0          0          0   
programm            0         0          0          0          0          0   
research     0.529541  0.350635   0.252533   0.120233   0.120233   0.120233   
tabik               0         0          0          0          0          0   
garcía              0         0          0          0          0          0   
project             0         0          0          0          0          0   
decsai              0         0          0          0          0          0   

              psycholog   strategi misinform      polit support       2020  \
misinfodem     0.360699   0.360699  0.505066   0.409223       0   0.468991   
covid          0.235213   0.235213  0.315666   0.240466       0   0.293937   
articl        0.0784044  0.0784044  0.284614   0.126266       0     0.1894   
mechan         0.185806   0.185806  0.115951  0.0762243       0  0.0784044   
health         0.185806   0.185806  0.115951  0.0762243       0  0.0784044   
commun         0.185806   0.185806  0.115951  0.0762243       0  0.0784044   
psycholog   3.72529e-09          0  0.300211   0.115951       0   0.170443   
strategi              0          0  0.300211   0.115951       0   0.170443   
misinform      0.300211   0.300211         0   0.185806       0   0.152449   
polit          0.115951   0.115951  0.185806          0       0  0.0631332   
support               0          0         0          0       0          0   
2020           0.170443   0.170443  0.152449  0.0631332       0          0   
granada               0          0         0          0       0          0   
programm              0          0         0          0       0          0   
research       0.229409   0.229409   0.14117   0.126266       0  0.0631332   
tabik                 0          0         0          0       0          0   
garcía                0          0         0          0       0          0   
project               0          0         0          0       0          0   
decsai                0          0         0          0       0          0   

           granada programm   research tabik garcía project decsai  
misinfodem       0        0   0.529541     0      0       0      0  
covid            0        0   0.350635     0      0       0      0  
articl           0        0   0.252533     0      0       0      0  
mechan           0        0   0.120233     0      0       0      0  
health           0        0   0.120233     0      0       0      0  
commun           0        0   0.120233     0      0       0      0  
psycholog        0        0   0.229409     0      0       0      0  
strategi         0        0   0.229409     0      0       0      0  
misinform        0        0    0.14117     0      0       0      0  
polit            0        0   0.126266     0      0       0      0  
support          0        0          0     0      0       0      0  
2020             0        0  0.0631332     0      0       0      0  
granada          0        0          0     0      0       0      0  
programm         0        0          0     0      0       0      0  
research         0        0          0     0      0       0      0  
tabik            0        0          0     0      0       0      0  
garcía           0        0          0     0      0       0      0  
project          0        0          0     0      0       0      0  
decsai           0        0          0     0      0       0      0  

(-0.16544931052577933, 0.8724, -1.1200916601203454)

mantel_test_n(5)

Pairwise-distance
         market rate juli     covid june   support      2020    granada  \
market        0    0    0         0    0         0         0          0   
rate          0    0    0         0    0         0         0          0   
juli          0    0    0         0    0         0         0          0   
covid         0    0    0         0    0  0.608033  0.282616   0.457677   
june          0    0    0         0    0         0         0          0   
support       0    0    0  0.608033    0         0  0.327071   0.161988   
2020          0    0    0  0.282616    0  0.327071         0   0.175514   
granada       0    0    0  0.457677    0  0.161988  0.175514          0   
programm      0    0    0  0.526543    0  0.126325  0.246469  0.0742376   

           programm  
market            0  
rate              0  
juli              0  
covid      0.526543  
june              0  
support    0.126325  
2020       0.246469  
granada   0.0742376  
programm          0  
Pairwise-distance
            market      rate       juli      covid      june support  \
market           0  0.197323   0.172313   0.172313  0.323988       0   
rate      0.197323         0   0.331629   0.331629  0.129455       0   
juli      0.172313  0.331629          0          0  0.459925       0   
covid     0.172313  0.331629          0          0  0.459925       0   
june      0.323988  0.129455   0.459925   0.459925         0       0   
support          0         0          0          0         0       0   
2020      0.174022  0.286306  0.0801315  0.0801315  0.409594       0   
granada          0         0          0          0         0       0   
programm         0         0          0          0         0       0   

               2020 granada programm  
market     0.174022       0        0  
rate       0.286306       0        0  
juli      0.0801315       0        0  
covid     0.0801315       0        0  
june       0.409594       0        0  
support           0       0        0  
2020              0       0        0  
granada           0       0        0  
programm          0       0        0  

(-0.3211636322477467, 0.97417, -1.4414045746819804)

mantel_test_n(10)

Pairwise-distance
         market rate juli     covid june also friday job infect million  \
market        0    0    0         0    0    0      0   0      0       0   
rate          0    0    0         0    0    0      0   0      0       0   
juli          0    0    0         0    0    0      0   0      0       0   
covid         0    0    0         0    0    0      0   0      0       0   
june          0    0    0         0    0    0      0   0      0       0   
also          0    0    0         0    0    0      0   0      0       0   
friday        0    0    0         0    0    0      0   0      0       0   
job           0    0    0         0    0    0      0   0      0       0   
infect        0    0    0         0    0    0      0   0      0       0   
million       0    0    0         0    0    0      0   0      0       0   
support       0    0    0  0.608033    0    0      0   0      0       0   
2020          0    0    0  0.282616    0    0      0   0      0       0   
granada       0    0    0  0.457677    0    0      0   0      0       0   
programm      0    0    0  0.526543    0    0      0   0      0       0   
research      0    0    0  0.526543    0    0      0   0      0       0   
tabik         0    0    0  0.421418    0    0      0   0      0       0   
garcía        0    0    0  0.318922    0    0      0   0      0       0   
project       0    0    0  0.492938    0    0      0   0      0       0   
decsai        0    0    0  0.492938    0    0      0   0      0       0   

           support       2020    granada   programm   research      tabik  \
market           0          0          0          0          0          0   
rate             0          0          0          0          0          0   
juli             0          0          0          0          0          0   
covid     0.608033   0.282616   0.457677   0.526543   0.526543   0.421418   
june             0          0          0          0          0          0   
also             0          0          0          0          0          0   
friday           0          0          0          0          0          0   
job              0          0          0          0          0          0   
infect           0          0          0          0          0          0   
million          0          0          0          0          0          0   
support          0   0.327071   0.161988   0.126325   0.126325   0.220392   
2020      0.327071          0   0.175514   0.246469   0.246469   0.148475   
granada   0.161988   0.175514          0  0.0742376  0.0742376  0.0631623   
programm  0.126325   0.246469  0.0742376          0          0   0.109024   
research  0.126325   0.246469  0.0742376          0          0   0.109024   
tabik     0.220392   0.148475  0.0631623   0.109024   0.109024          0   
garcía    0.323975  0.0843519   0.161988   0.218047   0.218047   0.109024   
project   0.189487   0.222713  0.0843519  0.0631623  0.0631623  0.0742376   
decsai    0.189487   0.222713  0.0843519  0.0631623  0.0631623  0.0742376   

             garcía    project     decsai  
market            0          0          0  
rate              0          0          0  
juli              0          0          0  
covid      0.318922   0.492938   0.492938  
june              0          0          0  
also              0          0          0  
friday            0          0          0  
job               0          0          0  
infect            0          0          0  
million           0          0          0  
support    0.323975   0.189487   0.189487  
2020      0.0843519   0.222713   0.222713  
granada    0.161988  0.0843519  0.0843519  
programm   0.218047  0.0631623  0.0631623  
research   0.218047  0.0631623  0.0631623  
tabik      0.109024  0.0742376  0.0742376  
garcía            0   0.175514   0.175514  
project    0.175514          0          0  
decsai     0.175514          0          0  
Pairwise-distance
            market       rate       juli      covid       june       also  \
market           0   0.197323   0.172313   0.172313   0.323988   0.104544   
rate      0.197323          0   0.331629   0.331629   0.129455   0.204797   
juli      0.172313   0.331629          0          0   0.459925   0.129455   
covid     0.172313   0.331629          0          0   0.459925   0.129455   
june      0.323988   0.129455   0.459925   0.459925          0   0.331629   
also      0.104544   0.204797   0.129455   0.129455   0.331629          0   
friday    0.104544   0.204797   0.129455   0.129455   0.331629          0   
job       0.160263  0.0861564   0.258909   0.258909   0.204797   0.129455   
infect    0.160263  0.0861564   0.258909   0.258909   0.204797   0.129455   
million   0.271949  0.0801315   0.388364   0.388364  0.0861564   0.258909   
support          0          0          0          0          0          0   
2020      0.174022   0.286306  0.0801315  0.0801315   0.409594  0.0861564   
granada          0          0          0          0          0          0   
programm         0          0          0          0          0          0   
research         0          0          0          0          0          0   
tabik            0          0          0          0          0          0   
garcía           0          0          0          0          0          0   
project          0          0          0          0          0          0   
decsai           0          0          0          0          0          0   

             friday        job     infect    million support       2020  \
market     0.104544   0.160263   0.160263   0.271949       0   0.174022   
rate       0.204797  0.0861564  0.0861564  0.0801315       0   0.286306   
juli       0.129455   0.258909   0.258909   0.388364       0  0.0801315   
covid      0.129455   0.258909   0.258909   0.388364       0  0.0801315   
june       0.331629   0.204797   0.204797  0.0861564       0   0.409594   
also              0   0.129455   0.129455   0.258909       0  0.0861564   
friday            0   0.129455   0.129455   0.258909       0  0.0861564   
job        0.129455          0          0   0.129455       0   0.204797   
infect     0.129455          0          0   0.129455       0   0.204797   
million    0.258909   0.129455   0.129455          0       0   0.331629   
support           0          0          0          0       0          0   
2020      0.0861564   0.204797   0.204797   0.331629       0          0   
granada           0          0          0          0       0          0   
programm          0          0          0          0       0          0   
research          0          0          0          0       0          0   
tabik             0          0          0          0       0          0   
garcía            0          0          0          0       0          0   
project           0          0          0          0       0          0   
decsai            0          0          0          0       0          0   

         granada programm research tabik garcía project decsai  
market         0        0        0     0      0       0      0  
rate           0        0        0     0      0       0      0  
juli           0        0        0     0      0       0      0  
covid          0        0        0     0      0       0      0  
june           0        0        0     0      0       0      0  
also           0        0        0     0      0       0      0  
friday         0        0        0     0      0       0      0  
job            0        0        0     0      0       0      0  
infect         0        0        0     0      0       0      0  
million        0        0        0     0      0       0      0  
support        0        0        0     0      0       0      0  
2020           0        0        0     0      0       0      0  
granada        0        0        0     0      0       0      0  
programm       0        0        0     0      0       0      0  
research       0        0        0     0      0       0      0  
tabik          0        0        0     0      0       0      0  
garcía         0        0        0     0      0       0      0  
project        0        0        0     0      0       0      0  
decsai         0        0        0     0      0       0      0  

(-0.24513328801204826, 0.99907, -1.8270394036391986)

3. Paragraph level

LSA on covid-science.txt at paragraph level

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            if text !='':
                documents_list.append(text)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science.txt")

Number of words in text file : 120257
Total Number of Documents: 14379

#data cleaning
clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet']
['methodology', 'predicting', 'covid']
['based', 'chest', 'images']
['tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte']
['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'herrera']
['abstract', 'currently', 'coronavirus', 'disease', 'covid']
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['have', 'great', 'potential', 'building', 'covid', 'triage', 'systems']
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['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova']
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['digital', 'object', 'identifier', '1109', 'jbhi', '2020', '3037127']
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[]
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['softmax']
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[]
[]
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[]
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[]
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[]
[]
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[]
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[]
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[]
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[]
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[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['kerala']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['1208']
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[]
[]
[]
[]
[]
[]
[]
['1272']
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[]
['15953']
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['delhi']
[]
[]
[]
[]
[]
[]
[]
['1707']
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[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['1355']
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[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['count']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['mean']
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
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[]
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['automatically', 'operators', 'that', 'support', 'them', 'take', 'action']
['real', 'time', 'continue', 'maintain', 'support', 'this', 'project']
['long', 'make', 'sure', 'working', 'normally']
['effectively']
['figure', 'overall', 'performance', 'covid', 'scraper']
['understand', 'detailed', 'performance', 'covid', 'scraper', 'data', 'from', 'different', 'countries', 'countries', 'have']
['been', 'selected', 'including', 'austria', 'chile', 'jamaica', 'panama']
['bosnia', 'hungary', 'lanka', 'turkey', 'slovenia', 'switzer', 'land', 'every', 'those', 'countries', 'share', 'same', 'data', 'type']
['hence', 'five', 'types', 'data', 'scraping', 'performance', 'could']
['tested', 'every', 'country', 'times', 'average']
['time', 'calculated', 'reduce', 'randomness', 'austria', 'chile']
['publish', 'data', 'table', 'format', 'average', 'processing', 'time']
['seconds', 'respectively', 'though', 'they']
['same', 'format', 'reason', 'difference', 'processing', 'time']
['primarily', 'difference', 'size', 'crawling', 'data']
['austria', 'data', 'size', 'whereas', 'chile']
['data', 'size', 'this', 'reason', 'takes']
['more', 'time', 'process', 'chile', 'data', 'comparison', 'austria']
['data', 'addition', 'downloading', 'speed', 'during', 'processing']
['time', 'also', 'contributes', 'difference', 'jamaica']
['panama', 'data', 'json', 'show', 'average', 'time']
['seconds', 'respectively', 'similar', 'austria']
['chile', 'json', 'file', 'size', 'those', 'countries', 'major']
['reason', 'time', 'difference', 'file', 'size', 'jamaica']
['panama', 'respectively', 'bosnia']
['hungary', 'publish', 'data', 'image', 'format', 'take', 'average']
['time', 'seconds', 'file', 'size', 'hungary', 'greater']
['than', 'bosnia', 'which', 'contributes', 'more', 'processing', 'time']
['hungary', 'data', 'source', 'lanka', 'turkey']
['format', 'difference', 'processing', 'time', 'between', 'those']
['countries', 'primarily', 'reasons', 'first']
['lanka', 'crawling', 'script', 'directly', 'scrapes', 'data', 'from']
['current', 'data', 'file', 'turkey', 'script', 'first', 'crawls']
['html', 'page', 'retrieve', 'latest', 'file', 'link', 'which']
['then', 'scraps', 'desired', 'data', 'from', 'which', 'takes', 'more']
['time', 'process', 'second', 'required', 'data', 'lanka']
['first', 'page', 'published', 'file', 'whereas', 'turkey']
['desired', 'data', 'third', 'page', 'during', 'performance']
['testing', 'which', 'results', 'need', 'crawling', 'more', 'pages', 'than']
['lanka', 'slovenia', 'switzerland', 'data', 'source']
['xlsx', 'format', 'with', 'file', 'size', 'respectively']
['processing', 'time', 'slovenia', 'more', 'than', 'switzerland']
['because', 'file', 'size', 'larger', 'hence', 'downloading', 'time']
['increases', 'causing', 'increase', 'processing', 'time']
['processing', 'time', 'countries', 'mainly', 'depends']
['complexity', 'published', 'website', 'data', 'files', 'size', 'data']
['sources', 'internet', 'speed']
['figure', 'performance', 'tests', 'single', 'countries', 'with', 'different', 'data']
['types']
['cases', 'with', 'scraped', 'data', 'product']
['data', 'generated', 'covid', 'scraper', 'been', 'used']
['support', 'much', 'scientific', 'research', 'within', 'academic', 'munity', 'studies', 'introduced', 'here', 'applying']
['data', 'generated', 'covid', 'scraper', 'major', 'data']
['sources']
['medical', 'resource', 'deficiency', 'dynamics']
['since', 'late', 'march', '2021', 'over', 'million', 'popu', 'lation', 'been', 'tested', 'positive', 'result', 'covid']
['whether', 'medical', 'resources', 'were', 'enough', 'handle', 'worst']
['scenario', 'amid', 'crisis', 'discussed', 'evaluated', 'public']
['good', 'three', 'elements', 'including', 'ventilators', 'beds']
['critical', 'medical', 'care', 'staff', 'were', 'reported', 'fundamental']
['volume', '2021', '84793h', 'covid', 'scraper', 'open', 'source', 'toolset']
['medical', 'resources', 'support', 'critically', 'patients', 'this']
['study', 'authors', 'have', 'created', 'medical', 'resource', 'deficiency']
['index', 'mrdi', 'using', 'covid', 'scraper', 'data', 'product']
['related', 'covid', 'medical', 'data', 'measure', 'reality']
['medical', 'burden', 'using', 'crawled', 'confirmed', 'death']
['recovered', 'hospitalized', 'viral', 'cases', 'county', 'level']
[]
['mrdi', 'defined', 'division', 'daily', 'active', 'cases']
['medical', 'resources', 'county', 'scale', 'while', 'daily', 'active']
['cases', 'refer', 'difference', 'accumulated', 'number', 'firmed', 'positive', 'tested', 'patients', 'with', 'accumulated', 'number']
['deaths', 'medical', 'resources', 'were', 'calculated']
['number', 'licensed', 'beds', 'multiplied', 'total', 'number']
['critical', 'care', 'staff', 'specifically', 'covid', 'response']
['higher', 'value', 'mrdi', 'medical', 'source', 'certain']
['area', 'pressed', 'harder', 'accumulated', 'viral', 'case', 'numbers']
['positive', 'confirmed', 'deaths', 'were', 'extracted', 'from']
['facts', 'cross', 'validated', 'with', 'sources', 'from', 'john', 'hopkins']
['university', 'hospital', 'licensed', 'number', 'critical', 'ical', 'care', 'staff', 'with', 'comprehensive', 'specialty', 'were', 'accessed']
['from', 'definitive', 'healthcare', 'consulting', 'services', 'national']
['provider', 'identifier', 'registry', 'database', 'respectively']
['data', 'collected', 'this', 'study', 'converted', 'into', 'county', 'scale']
['with', 'unique', 'identifier', 'county', 'code', 'census', 'standard']
['monitor', 'share', 'dynamic', 'heterogeneity', 'infor', 'mation', 'medical', 'resource', 'distribution', 'medical', 'resource']
['deficiency', 'dashboard', 'created', 'based', 'arcgis', 'dash', 'board', 'analyzing', 'visualizing', 'generated', 'results']
['figure', 'bubble', 'center', 'dashboard']
['represents', 'spatial', 'distribution', 'mdri', 'where', 'area']
['circle', 'refers', 'index', 'value', 'lists', 'counties']
['displayed', 'right', 'show', 'statistics', 'rank', 'mrdi']
['infection', 'risk', 'rate', 'which', 'interactively', 'generated', 'based']
['selected', 'extend', 'indicator']
['charts', 'fraction', 'hospital', 'types', 'medical', 'care']
['staff', 'applied', 'display', 'each', 'county', 'left']
['dashboard', 'track', 'temporal', 'pattern', 'index', 'line']
['chart', 'built', 'bottom', 'demonstrate', 'time', 'series']
['analysis', 'result', 'selected', 'area']
['figure', 'scraped', 'data', 'product', 'monitor', 'medical', 'resource']
['deficiency', 'dynamics', 'covid']
['impact', 'social', 'distancing', 'measures']
['covid', 'cases', 'mortality']
['another', 'study', 'impact', 'control', 'policies', 'using']
['covid', 'scraper', 'data', 'corresponding', 'policies', 'dataset']
['this', 'study', 'authors', 'analyzed', 'series', 'social', 'distancing']
['policies', 'including', 'school', 'closure', 'workplace', 'closure', 'cancel', 'lation', 'public', 'events', 'public', 'information', 'campaigns', 'cancel']
['public', 'transport', 'internal', 'movement', 'restriction', 'travel']
['control', 'that', 'have', 'been', 'implemented', 'combat', 'world', 'wide', 'pandemic', 'previous', 'studies', 'have', 'found', 'social', 'distancing']
['policies', 'effective', 'mitigating', 'covid']
['however', 'these', 'policies', 'have', 'negative', 'impacts', 'economic']
['development', 'normal', 'life', 'limited', 'understanding']
['effectiveness', 'each', 'individual', 'policy', 'posed', 'grand']
['challenges', 'reopening', 'process', 'which', 'stringency']
['social', 'distancing', 'reduced', 'balance', 'health', 'develop', 'ment', 'study', 'investigating', 'effectiveness', 'seven', 'major']
['social', 'distancing', 'policies', 'covid', 'case']
['mortality', 'growth', 'rate', 'conducted', 'using', 'case', 'data']
['collected', 'policy', 'data', 'shared', 'oxford', 'policy', 'tracker']
['project', 'estimate', 'temporal', 'dynamic', 'impact']
['policies', 'covid', 'cases', 'policy', 'data', 'transformed']
['variables', 'which', 'represent', 'policy', 'implementation']
['periods', 'including', 'week', 'weeks', 'three', 'weeks']
['month', 'months', 'more', 'than', 'months', 'scraped']
['daily', 'cumulative', 'case', 'data', 'were', 'converted', 'daily', 'case']
['growth', 'rate', 'which', 'difference', 'between', 'logarithms']
['cumulative', 'case', 'numbers', 'successive', 'days', 'these']
['implementation', 'indicators', 'were', 'regressed', 'case', 'growth', 'rate']
['using', 'panel', 'regression', 'analysis', 'panel', 'regression', 'widely']
['used', 'analyze', 'dimensional', 'panel', 'data', 'which', 'typically']
['cross', 'sectional', 'states', 'countries', 'longitudinal']
['year', 'month', 'dimensions', 'specifically', 'fixed', 'effects']
['panel', 'regression', 'model', 'adopted', 'study', 'could']
['model', 'unobserved', 'heterogeneity', 'through', 'state', 'specific', 'fixed']
['effects', 'addition', 'growth', 'rate', 'multiplied']
['regression', 'thus', 'regression', 'coefficient', 'policy']
['could', 'interpreted', 'percentage', 'point', 'changes', 'growth']
['rate', 'figure']
['figure', 'scraped', 'data', 'product', 'support', 'covid', 'policy']
['analysis']
['study', 'demonstrated', 'that', 'stay', 'home', 'orders']
['workplace', 'closures', 'public', 'information', 'campaigns']
['drastically', 'decrease', 'confirmed', 'case', 'growth', 'rate', 'stay', 'home', 'orders', 'workplace', 'closure', 'decrease', 'case', 'growth', 'rate']
['through', 'changes', 'mobility', 'while', 'public', 'information', 'paign', 'impact', 'confirmed', 'case', 'growth', 'rate', 'through', 'channels']
['other', 'than', 'mobility', 'addition', 'regarding', 'death', 'case', 'growth']
['rate', 'stay', 'home', 'orders', 'international', 'travel', 'controls']
['limited', 'mitigation', 'effect', 'relation', 'between', 'policies']
['84794', 'volume', '2021h', 'covid', 'scraper', 'open', 'source', 'toolset']
['case', 'growth', 'rates', 'learned', 'study', 'could', 'provide']
['policymakers', 'better', 'understanding', 'effectiveness']
['each', 'policy', 'support', 'decision', 'making']
['conclusion']
['covid', 'outbreak', 'impacted', 'billions', 'people', 'over']
['world', 'governments', 'organizations', 'research', 'insti', 'tutions', 'conducting', 'rapid', 'research', 'covid', 'related']
['problems', 'that', 'bring', 'people', 'every', 'country', 'back']
['normalcy', 'detailed', 'spatiotemporal', 'covid', 'records', 'data']
['proved', 'important', 'evidence', 'support', 'covid']
['related', 'research', 'however', 'collect', 'aggregate', 'store']
['share', 'data', 'published', 'each', 'country', 'world']
['community', 'effectively', 'challenge', 'solve', 'this', 'prob', 'covid', 'scraper', 'developed', 'open', 'sourced']
['toolset', 'that', 'automatically', 'scan', 'extract', 'collect', 'filter']
['refine', 'unify', 'store', 'public', 'spatiotemporal', 'covid']
['records', 'fifty', 'eight', 'countries', 'around', 'world', 'which']
['provide', 'available', 'covid', 'data', 'sources', 'with', 'minor']
['code', 'adjustments', 'this', 'toolset', 'accommodate', 'various', 'types']
['data', 'published', 'each', 'country', 'various', 'data', 'formats']
['scales', 'channels', 'publish', 'frequencies', 'more', 'importantly']
['countries', 'that', 'provide', 'access', 'historical']
['covid', 'data', 'automatically', 'build', 'historical', 'data', 'lections', 'support', 'research', 'repeatedly', 'certain', 'frequency']
['covid', 'scraper', 'processes', 'high', 'effective', 'manner']
['collecting', 'data', 'from', 'countries', 'over', 'world', 'within', 'single']
['about', 'minutes', 'after', 'post', 'processing', 'data']
['cleaning', 'fetched', 'data', 'unified', 'saved', 'into', 'database']
['sharing', 'with', 'daily', 'data', 'quality', 'checking', 'data', 'product']
['production', 'global', 'covid', 'data', 'github', 'repository']
['been', 'maintained', 'since', 'march', '2020', 'addition', 'visual', 'ization', 'component', 'developed', 'covid', 'scraper']
['publish', 'data', 'product', 'service', 'public', 'view']
['access']
['covid', 'scraper', 'utilized', 'scraping', 'technolo', 'gies', 'that', 'used', 'data', 'science', 'related', 'fields']
['integrating', 'open', 'source', 'packages', 'tools', 'data']
['extracting', 'network', 'simulation', 'file', 'image', 'parsing', 'work', 'flow', 'automation', 'covid', 'scraper', 'highly', 'flexible']
['automatic', 'toolset', 'that', 'process', 'tasks', 'unsupervised', 'under']
['users', 'settings', 'with', 'nature', 'open', 'source', 'users']
['easily', 'customize', 'data', 'sources', 'data', 'structure']
['output', 'data', 'product', 'execution', 'logic', 'processing', 'frequency']
['exception', 'handling', 'addition', 'users', 'modify']
['source', 'code', 'extend', 'collecting', 'datasets', 'other']
['purposes', 'support', 'wider', 'studies', 'tasks', 'such', 'emer', 'gency', 'response', 'natural', 'disaster', 'detection', 'saving']
['lives']
['currently', 'limitation', 'that', 'data', 'quality', 'control']
['validation', 'cannot', 'fully', 'automated', 'because', 'accuracy']
['parsing', 'text', 'extracting', 'cannot', 'always', 'guaranteed']
['using', 'current', 'packages', 'hence', 'users', 'need', 'intervene']
['data', 'quality', 'control', 'process', 'image', 'type']
['data', 'make', 'sure', 'data', 'product', 'high', 'quality', 'with']
['rapid', 'development', 'text', 'parsing', 'from', 'images', 'will', 'keep']
['updating', 'this', 'component', 'minimize', 'human', 'intervention']
['automation', 'process']

# LSA Model
number_of_topics=100
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

#top 5 words from lsa
dict(model_science.show_topic(0, topn=5))

{'covid': 0.7824307537118228,
 'imag': 0.3483697703449934,
 'use': 0.19262982910237736,
 'data': 0.16979352187609095,
 'model': 0.1434213806427849}

#word cloud of top 10 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph10.png')

# cosine similarity of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled12.png')

cosine-similarity
             covid         imag          use         data        model
covid            1   0.00105585 -5.09149e-05   0.00048856  0.000807228
imag    0.00105585            1 -0.000564989  -0.00559808    0.0011847
use   -5.09149e-05 -0.000564989            1   8.0517e-05   0.00060903
data    0.00048856  -0.00559808   8.0517e-05            1 -0.000274743
model  0.000807228    0.0011847   0.00060903 -0.000274743            1

#networkx of top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig12.png')

LSA on covid-non-science-small.txt at 2-3 paragraph level

#data loading
document_list,titles,word_count_nscience=load_data("","covid-non-science.txt")

Number of words in text file : 8713
Total Number of Documents: 387

#data preprocessing
clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'reality']
['reuters']
['published', 'july', '2020', 'updated', 'july', '2020']
['marc', 'jones']
['london', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves']
['market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained']
['chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise']
['currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday']
['think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes']
['have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return', 'infection', 'rates', 'which', 'make', 'nervous']
['against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june']
['euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown']
['futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence']
['nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors']
['economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures']
['number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada']
['recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume']
['more', 'than', 'three', 'dozen', 'states', 'increases', 'covid', 'cases', 'with', 'cases', 'florida', 'spiking', 'above']
['nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'view', 'that', 'overall', 'situation', 'still', 'improving', 'overall']
['year', 'german', 'government', 'bond', 'yields', 'basis', 'points', 'this', 'week', 'their', 'biggest', 'weekly', 'rise', 'month', 'though', 'they', 'nudged', 'down', 'friday', 'riskier', 'italian', 'yields', 'fell', 'well', 'though', 'which', 'their', 'lowest', 'since', 'late', 'march']
['prices', 'also', 'eased', 'after', 'otherwise', 'solid', 'week', 'brent', 'crude', 'fell', 'barrel', 'while', 'crude', 'dropped', 'barrel', 'both', 'were', 'around', 'this', 'time', 'months']
['copper', 'prices', 'were', 'poised', 'seventh', 'consecutive', 'weekly', 'gain', 'their', 'longest', 'winning', 'streak', 'nearly', 'three', 'years', 'despite', 'slight', 'easing', 'after', 'supplier', 'chile', 'assured', 'traders', 'about', 'supply']
['three', 'month', 'copper', 'hovering', 'tonne', 'more', 'than', 'from', 'lows', 'ploughed', 'march']
['issue', 'that', 'hangs', 'over', 'markets', 'will', 'surge', 'secondary', 'infections', 'that', 'will', 'trigger', 'second', 'wave', 'national', 'rather', 'than', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote', 'note']
['reporting', 'marc', 'jones']
['newspaper']
['covid', 'vaccine', 'frontrunners']
['reuters']
['published', 'october', '2020', 'updated', 'october', '2020']
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['newspaper']
['giants', 'another', 'covid', 'case']
['reuters']
['published', 'november', '2020', 'updated', 'november', '2020']
['york', 'giants', 'have', 'another', 'case', 'covid']
['monday', 'night', 'were', 'notified', 'that', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'self', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'both', 'staff', 'members', 'were', 'identified', 'were', 'informed', 'remain', 'home', 'today']
['giants', 'entering', 'their', 'week', 'play', 'week', 'this', 'second', 'time', 'team', 'player', 'test', 'positive', 'covid']
['prior', 'playing', 'tampa', 'buccaneers', 'week', 'offensive', 'lineman', 'will', 'hernandez', 'tested', 'positive']
['hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'list', 'returned', 'team', 'last', 'week', 'played', 'sunday', 'victory', 'over', 'philadelphia', 'eagles']
['field', 'level', 'media']
['newspaper']
['covid', 'antivirals', 'taskforce', 'launched']
['media']
['published', 'april', '2021', 'updated', 'april', '2021']
['home', 'covid', 'treatments', 'being', 'sought', 'team', 'government', 'experts', 'speed', 'recovery', 'time', 'people', 'become', 'infected', 'with', 'virus']
['government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid']
['prime', 'minister', 'boris', 'johnson', 'said', 'group', 'will', 'seek', 'medicines', 'stop', 'covid', 'tracks']
['hoped', 'that', 'antivirals', 'could', 'help', 'reduce', 'infections', 'limit', 'impact', 'variants']
['they', 'also', 'help', 'protect', 'people', 'cannot', 'take', 'vaccines', 'those', 'fully', 'protected', 'after', 'having']
['officials', 'hope', 'find', 'bring', 'treatments', 'online', 'this', 'year']
['this', 'could', 'mean', 'that', 'people', 'with', 'covid', 'those', 'they', 'have', 'been', 'close', 'contact', 'with', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'form', 'stop', 'infection', 'spreading', 'speed', 'recovery', 'time']
['antivirals', 'taskforce', 'which', 'appoint', 'chair', 'will', 'search', 'most', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken', 'home']
['taskforce', 'will', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'also', 'look', 'manufacturing', 'opportunities']
['antiviral', 'drugs', 'type', 'medication', 'used', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses']
['success', 'vaccination', 'programme', 'demonstrated', 'what', 'achieve', 'when', 'bring', 'together', 'brightest', 'minds', 'said', 'johnson']
['antivirals', 'taskforce', 'will', 'seek', 'develop', 'innovative', 'treatments', 'take', 'home', 'stop', 'covid', 'tracks']
['these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'save', 'more', 'lives']
['health', 'secretary', 'matt', 'hancock', 'said', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'ones', 'from', 'this', 'terrible', 'virus']
['modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'have', 'played', 'crucial', 'part', 'response', 'pandemic', 'bringing', 'together', 'team', 'that', 'will', 'supercharge', 'search', 'antiviral', 'treatments', 'roll', 'them', 'soon', 'autumn']
['government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'said', 'speed', 'which', 'vaccines', 'therapeutics', 'such', 'dexamethasone', 'have', 'been', 'identified', 'deployed', 'against', 'covid', 'been', 'critical', 'pandemic', 'response']
['antivirals', 'tablet', 'form', 'another', 'tool', 'response']
['they', 'could', 'help', 'protect', 'those', 'protected', 'ineligible', 'vaccines']
['they', 'could', 'also', 'another', 'layer', 'defence', 'face', 'variants', 'concern']
['nikita', 'kanani', 'medical', 'director', 'primary', 'care', 'england', 'said', 'research', 'into', 'reality', 'record', 'speed', 'during', 'pandemic', 'this', 'taskforce', 'will', 'help', 'identify', 'roll', 'even', 'more', 'convenient', 'treatments', 'patients', 'with', 'covid']
['commenting', 'announcement', 'penny', 'ward', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'said', 'antiviral', 'treatment', 'influenza', 'been', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'even', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines']
['said', 'that', 'there', 'number', 'early', 'phase', 'trials', 'looking', 'oral', 'antiviral', 'medications']
['newspaper']
['african', 'covid', 'deaths']
[]
['published', 'february', '2021', 'updated', 'february', '2021']
['africa', 'thursday', 'recorded', 'more', 'than', 'deaths', 'from', 'covid', 'grim', 'milestone', 'likely', 'understate', 'real', 'toll', 'continent', 'billion', 'people', 'battles', 'second', 'wave', 'infections']
['countries', 'region', 'have', 'death', 'toll', 'from', 'reported', 'cases', 'according', 'tally']
['continent', 'relatively', 'spared', 'pandemic', 'last', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april', '2020']
['south', 'africa', 'worst', 'african', 'country', 'rolled', 'mass', 'testing', 'campaign', 'start', 'pandemic']
['date', 'country', 'recorded', 'nearly', 'million', 'cases', 'more', 'than', 'deaths']
['those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'only', 'reflect', 'fraction', 'actual', 'case', 'load', 'health', 'specialists']
['cases', 'clearly', 'under', 'reported', 'because', 'poor', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'also', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health', 'told']
['understaffed', 'health', 'facilities', 'lack', 'means', 'have', 'meant', 'many', 'african', 'countries', 'have', 'been', 'unable', 'mass', 'testing']
['many', 'countries', 'have', 'mainly', 'tests', 'capitals', 'further', 'moves', 'away', 'from', 'urban', 'centres', 'less', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'from', 'doctors', 'without', 'borders']
['disease', 'that', 'unnoticed', 'with', 'asymptomatic', 'patients', 'with', 'symptoms', 'that', 'confused', 'with', 'others', 'added']
['covid', 'found', 'pawpaw']
['zimbabwe', 'country', 'with', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'with', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains']
['tanzania', 'stopped', 'testing', '2020', 'after', 'claiming', 'found', 'positive', 'covid', 'case', 'pawpaw', 'quail', 'even', 'goat', 'tanzanian', 'government', 'last', 'released', 'official', 'figures', 'april']
['someone', 'told', 'year', 'that', 'continent', 'would', 'deaths', 'from', 'this', 'infection', 'probably', 'would', 'have', 'believed', 'john', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'told', 'reporters', 'thursday']
['toll', 'africa', 'however', 'significantly', 'lower', 'than', 'europe', 'world', 'most', 'affected', 'region', 'with', 'deaths', 'recorded', 'other', 'regions', 'that', 'badly', 'latin', 'america', 'with', 'deaths', 'united', 'states', 'canada', 'deaths']
['after', 'sharp', 'increase', 'january', 'africa', 'figures', 'have', 'fallen', 'sharply', 'past', 'weeks', 'over', 'last', 'seven', 'days', 'continent', 'recorded', 'deaths', 'drop', 'percent', 'from', 'previous', 'week']
['height', 'pandemic', 'january', 'continent', 'deaths']
['disaster']
['while', 'coronavirus', 'figures', 'clearly', 'underestimated', 'have', 'seen', 'health', 'disaster', 'africa', 'date', 'said', 'baron']
['several', 'studies', 'antibodies', 'which', 'make', 'possible', 'detect', 'whether', 'person', 'recovered', 'previously', 'been', 'exposed', 'virus', 'underway', 'many', 'african', 'countries', 'should', 'provide', 'better', 'idea', 'impact', 'pandemic', 'region']
['south', 'africa', 'where', 'almost', 'latest', 'cases', 'attributed', 'variant', 'virus', 'known', 'more', 'contagious', 'which', 'spread', 'widely', 'represents', 'nearly', 'half', 'deaths', 'reported', 'cases', 'continent']
['other', 'african', 'countries', 'that', 'most', 'affected', 'egypt', 'deaths', 'from', 'cases', 'morocco', 'deaths', 'from', 'cases']
['south', 'africa', 'also', 'country', 'with', 'most', 'covid', 'fatalities', 'continent', 'counting', 'deaths', 'inhabitants', 'ahead', 'tunisia', 'deaths', 'eswatini']
['lagging', 'behind', 'vaccination', 'race', 'continent', 'leading', 'industrial', 'powerhouse', 'administered', 'first', 'vaccines', 'wednesday']
['globally', 'covid', 'caused', 'more', 'than', 'million', 'infections', 'over', 'million', 'deaths', 'since', 'start', 'epidemic', 'wuhan', 'china', 'december', '2019']
[]
['newspaper']
['gyms', 'aren', 'covid', 'hotspots', 'industry']
['australian', 'associated', 'press']
['published', 'august', '2020', 'updated', 'august', '2020']
['fitness', 'industry', 'back', 'perception', 'that', 'gyms', 'covid', 'hotspots', 'saying', 'there', 'been', 'transmission', 'virus', 'linked', 'gyms']
['fitness', 'australia', 'chief', 'executive', 'barrie', 'elvish', 'said', 'fitness', 'australia', 'gathered', 'data', 'from', 'random', 'sample', 'operators', 'that', 'proves', 'gyms', 'safe']
['since', 'gyms', 'reopened', 'june', 'there', 'been', 'more', 'than', 'million', 'visits', 'gyms', 'with', 'recorded', 'community', 'transmission', 'said']
['same', 'cannot', 'said', 'restaurants', 'bars', 'where', 'cluster', 'cases', 'have', 'started', 'spread', 'however', 'gyms', 'still', 'being', 'categorised', 'with', 'these', 'other', 'entertainment', 'venues', 'cast', 'negative', 'light', 'said', 'statement', 'thursday']
['however', 'misinformation', 'negative', 'commentary', 'portrayed', 'health', 'fitness', 'facilities', 'hotspots', 'covid', 'resulting', 'some', 'people', 'being', 'scared', 'exercise', 'their', 'said', 'despite', 'additional', 'precautions', 'stringent', 'hygiene', 'practices']
['fitness', 'australia', 'data', 'gathered', 'from', 'electronic', 'swipes', 'used', 'members', 'access', 'which', 'also', 'could', 'used', 'sophisticated', 'contact', 'tracing', 'should', 'required']
['elvish', 'said', 'there', 'been', 'reported', 'cases', 'where', 'member', 'visited', 'while', 'infected', 'with', 'covid', 'health', 'authorities', 'identified', 'cases', 'transmission']
['part', 'problem', 'actually', 'part', 'solution', 'getting', 'more', 'australians', 'more', 'active', 'more', 'often', 'help', 'prevent', 'long', 'term', 'lifestyle', 'related', 'disease', 'elvish', 'said']
['urging', 'government', 'work', 'with', 'fitness', 'australia', 'industry', 'ensure', 'health', 'safety', 'community']
['newspaper']
['covid', 'victims', 'maintain', 'immunity']
['australian', 'associated', 'press']
['published', 'november', '2020', 'updated', 'november', '2020']
['australian', 'researchers', 'have', 'discovered', 'that', 'patients', 'been', 'infected', 'with', 'covid', 'retain', 'immunity', 'against', 'virus', 'disease', 'least', 'eight', 'months']
['research', 'strongest', 'evidence', 'that', 'vaccines', 'against', 'virus', 'will', 'work', 'long', 'periods']
['previous', 'studies', 'found', 'first', 'wave', 'antibodies', 'produced', 'human', 'body', 'after', 'infection', 'waned', 'after', 'first', 'months', 'raising', 'concerns', 'that', 'people', 'could', 'quickly', 'lose', 'immunity']
['research', 'allays', 'those', 'concerns']
['study', 'result', 'collaboration', 'associate', 'professor', 'menno', 'zelm', 'from', 'monash', 'university', 'published', 'monday', 'preprint', 'server', 'medrxiv']
['researchers', 'found', 'specific', 'cell', 'within', 'human', 'immune', 'system', 'memory', 'cell', 'remembers', 'infection', 'virus', 'challenged', 'again', 'through', 'exposure', 'virus', 'triggers', 'protective', 'immune', 'response', 'through', 'rapid', 'production', 'protective', 'antibodies']
['researchers', 'recruited', 'covid', 'patients', 'took', 'blood', 'samples', 'between', 'four', 'post', 'infection', 'post', 'infection']
['with', 'other', 'studies', 'looking', 'antibody', 'response', 'researchers', 'found', 'that', 'antibodies', 'against', 'virus', 'started', 'drop', 'after', 'days', 'post', 'infection']
['however', 'patients', 'continued', 'have', 'memory', 'cells', 'that', 'recognised', 'components', 'virus', 'spike', 'nucleocapsid', 'proteins']
['these', 'virus', 'specific', 'memory', 'cells', 'were', 'present', 'long', 'eight', 'months', 'after', 'infection']
['associate', 'professor', 'zelm', 'said', 'results', 'gave', 'hope', 'efficacy', 'vaccine', 'against', 'virus', 'explained', 'there', 'been', 'examples', 'genuine', 'reinfection', 'millions', 'people', 'tested', 'positive', 'virus', 'globally']
['these', 'results', 'important', 'because', 'they', 'show', 'definitively', 'that', 'patients', 'infected', 'with', 'covid', 'virus', 'fact', 'retain', 'immunity', 'against', 'virus', 'disease', 'said']
['this', 'been', 'black', 'cloud', 'hanging', 'over', 'potential', 'protection', 'that', 'could', 'provided', 'covid', 'vaccine', 'gives', 'real', 'hope', 'that', 'once', 'vaccine', 'vaccines', 'developed', 'they', 'will', 'provide', 'long', 'term', 'protection']
['newspaper']
['origins', 'covid']
['reuters']
['published', 'january', '2021', 'updated', 'january', '2021']
['shanghai', 'reuters', 'team', 'from', 'world', 'health', 'organization', 'works', 'china', 'investigate', 'origins', 'covid', 'following', 'factbox', 'looks', 'what', 'know', 'about', 'pandemic', 'began']
['china', 'origins']
['coronavirus', 'that', 'causes', 'covid', 'known', 'sars', 'first', 'identified', 'central', 'chinese', 'city', 'wuhan', 'january', '2020', 'most', 'scientists', 'believe', 'still', 'most', 'likely', 'that', 'originated', 'china']
['peter', 'embarek', 'expert', 'food', 'safety', 'zoonotic', 'diseases', 'said', 'wuhan', 'offered', 'first', 'solid', 'clues', 'about', 'transmission', 'covid', 'investigation', 'would', 'start', 'there']
['though', 'some', 'studies', 'have', 'suggested', 'covid', 'present', 'italy', 'spain', 'earlier', '2019', 'that', 'might', 'have', 'been', 'responsible', 'spike', 'pneumonia', 'cases', 'france', 'researchers', 'believe', 'could', 'have', 'entered', 'wuhan', 'from', 'europe']
['closest', 'relative', 'nature', 'ratg13', 'virus', 'which', 'discovered', 'horseshoe', 'bats', 'southwest', 'china', 'yunnan', 'province', 'genetic', 'match', 'between', 'makes', 'highly', 'likely', 'that', 'covid', 'also', 'originated', 'colonies', 'china', 'southwest', 'border', 'regions']
['huanan', 'market']
['initial', 'cluster', 'infections', 'traced', 'back', 'huanan', 'seafood', 'market', 'wuhan', 'leading', 'many', 'assume', 'that', 'patient', 'zero', 'probably', 'trader', 'exposed', 'contaminated', 'meat', 'products']
['this', 'version', 'events', 'simplistic', 'explain', 'patterns', 'infection', 'wuhan', 'elsewhere']
['many', 'earliest', 'reported', 'cases', 'connection', 'with', 'market', '2019', 'wuhan', 'residents', 'were', 'hospitalised', 'with', 'what', 'turned', 'covid', 'them', 'link', 'huanan']
['team', 'chinese', 'researchers', 'said', 'there', 'were', 'types', 'sars', 'circulating', 'wuhan', 'only', 'which', 'associated', 'with', 'market']
['scientists', 'also', 'said', 'virus', 'unusually', 'adapted', 'rapid', 'human', 'transmission', 'making', 'unlikely', 'that', 'first', 'human', 'contact', 'made', 'seafood', 'market']
['intermediary', 'species']
['investigators', 'keen', 'find', 'what', 'intermediary', 'species', 'that', 'allowed', 'sars', 'move', 'from', 'original', 'host', 'into', 'humans']
['preliminary', 'scientific', 'papers', 'china', 'identified', 'snakes', 'mink', 'potential', 'candidates', 'similar', 'coronavirus', 'infections', 'were', 'also', 'found', 'pangolins', 'illegally', 'trafficked', 'into', 'china']
['some', 'scholars', 'believe', 'there', 'intermediary', 'species', 'that', 'sars', 'like', 'virus', 'transmitted', 'directly', 'from', 'bats', 'humans', 'possibly', 'multiple', 'occasions']
['first', 'people', 'infected', 'were', 'likely', 'traders', 'meat', 'droppings', 'used', 'traditional', 'chinese', 'medicine', 'them', 'could', 'have', 'carried', 'into', 'huanan', 'seafood', 'market', 'causing', 'superspreader', 'event', 'that', 'allowed', 'pandemic', 'begin']
['gain', 'function']
['though', 'there', 'credible', 'supporting', 'evidence', 'some', 'researchers', 'still', 'rule', 'possibility', 'that', 'virus', 'released', 'accidentally', 'specialist', 'wuhan', 'institute', 'virology']
['there', 'indication', 'that', 'sars', 'contains', 'synthetic', 'insertions', 'some', 'researchers', 'could', 'have', 'been', 'subject', 'process', 'known', 'gain', 'function', 'where', 'forces', 'virus', 'become', 'more', 'infectious', 'exposing', 'human', 'receptor', 'cells']
['some', 'experts', 'investigators', 'needs', 'granted', 'access', 'research', 'conducted', 'whether', 'there', 'closer', 'matches', 'than', 'ratg13', 'virus']
['there', 'another', 'explanation', 'coronavirus', 'proven', 'lethal']
['sars', 'been', 'circulating', 'humans', 'throughout', 'southwest', 'china', 'months', 'before', 'finally', 'identified', 'wuhan', 'natural', 'selection', 'itself', 'could', 'have', 'trained', 'bind', 'more', 'effectively', 'those', 'receptor', 'cells']
['fully', 'optimised', 'after', 'months', 'human', 'exposure', 'made', 'explosive', 'breakthrough', 'huanan', 'market', 'where', 'conditions', 'were', 'ideal', 'rapid', 'viral', 'transmission', 'though', 'also', 'very', 'likely', 'spreading', 'elsewhere', 'reporting', 'david', 'stanway', 'editing', 'gerry', 'doyle']
['newspaper']
['australian', 'covid', 'testing', 'criteria']
['australian', 'associated', 'press']
['published', 'april', '2020', 'updated', 'april', '2020']
['what', 'criteria', 'people', 'australia', 'need', 'meet', 'order', 'tested', 'covid']
['victoria']
['testing', 'expanded', 'victoria', 'include', 'anyone', 'experiencing', 'fever', 'acute', 'respiratory', 'symptoms']
['testing', 'available', 'anyone', 'recently', 'returned', 'from', 'travel', 'come', 'into', 'contact', 'with', 'confirmed', 'case']
['public', 'screening', 'clinics', 'have', 'opened', 'test', 'people', 'covid']
[]
['south', 'wales']
['testing', 'recommended', 'anyone', 'displaying', 'fever', 'acute', 'respiratory', 'infection', 'symptoms']
['overseas', 'travellers', 'past', 'days', 'cruise', 'ship', 'passengers', 'crew', 'members', 'those', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'case', 'urged', 'tested']
['sick', 'healthcare', 'aged', 'care', 'workers', 'recommended', 'tested', 'infection']
['people', 'living', 'penrith', 'sydney', 'inner', 'west', 'liverpool', 'randwick', 'waverley', 'woollahra', 'blacktown', 'cumberland', 'westmead', 'ryde', 'manning', 'lake', 'macquarie', 'urged', 'seek', 'testing', 'they', 'display', 'symptoms', 'community', 'community', 'transmission', 'occurred', 'these', 'areas']
[]
['queensland']
['people', 'displaying', 'fever', 'acute', 'respiratory', 'symptoms', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'case', 'have', 'been', 'overseas', 'within', 'past', 'days', 'tested']
['workers', 'vulnerable', 'setting', 'including', 'healthcare', 'aged', 'care', 'military', 'education', 'corrections', 'tested']
['people', 'live', 'brisbane', 'gold', 'coast', 'cairns', 'first', 'nations', 'community', 'also', 'tested', 'they', 'show', 'symptoms']
['people', 'travelling', 'from', 'declared', 'covid', 'hotspot', 'interstate', 'also', 'tested']
[]
['south', 'australia']
['residents', 'advised', 'tested', 'they', 'have', 'travelled', 'overseas', 'interstate', 'past', 'days']
['testing', 'advised', 'people', 'have', 'been', 'contact', 'with', 'confirmed', 'case', 'displaying', 'coronavirus', 'symptoms']
['healthcare', 'workers', 'with', 'direct', 'patient', 'contact', 'aged', 'residential', 'care', 'workers', 'have', 'fever', 'acute', 'respiratory', 'infection', 'shortness', 'breath', 'cough', 'sore', 'throat', 'urged', 'tested']
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[]
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[]
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[]
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['newspaper']
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['newspaper']
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['reuters']
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['newspaper']
['nears', 'covid', 'deaths']
[]
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['newspaper']
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['newspaper']
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['australian', 'associated', 'press']
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['newspaper']
['records', 'covid', 'cases', 'deaths']
['reuters']
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['newspaper']
['drugs', 'wrongly', 'touted', 'covid', 'treatments']
['australian', 'associated', 'press']
['published', 'august', '2020', 'updated', 'august', '2020']
['medicines', 'falsely', 'touted', 'covid', 'treatments']
['hydroxychloroquine', 'used', 'treat', 'malaria', 'rheumatoid', 'arthritis', 'lupus', 'there', 'reliable', 'evidence', 'prevent', 'treat', 'covid', 'some', 'studies', 'found', 'make', 'things', 'worse']
['remdesivir', 'antiviral', 'medicine', 'that', 'help', 'some', 'people', 'hospital', 'with', 'severe', 'covid', 'recover', 'faster', 'does', 'help', 'people', 'with', 'milder', 'covid']
['dexamethasone', 'reduces', 'inflammation', 'used', 'treat', 'asthma', 'arthritis', 'reduces', 'risk', 'dying', 'people', 'hospital', 'with', 'serious', 'covid', 'that', 'need', 'help', 'breathe', 'doesn', 'help', 'people', 'with', 'less', 'severe', 'covid', 'could', 'make', 'things', 'worse']
['ephedra', 'vitamin', 'other', 'complementary', 'medicines', 'none', 'found', 'effective', 'against', 'covid', 'outside', 'intensive', 'care']
['source', 'medicinewise']
['newspaper']
['covid', 'schools', 'study', 'breakdown']
['australian', 'associated', 'press']
['published', 'april', '2020', 'updated', 'april', '2020']
['what', 'national', 'centre', 'immunisation', 'research', 'surveillance', 'ncirs', 'study', 'into', 'covid', 'schools', 'found']
['total', 'students', 'staff', 'members', 'from', 'schools', 'were', 'from', 'march', 'april', '2020', 'diagnosed', 'with', 'covid']
['people', 'nine', 'were', 'students', 'nine', 'were', 'staff', 'members']
['those', 'people', 'total', 'close', 'contacts', 'within', 'school', 'over', 'that', 'time', 'period']
['close', 'contacts', 'just', 'people', 'caught', 'covid']
['those', 'people', 'both', 'students', 'pass', 'disease', 'others']
['students', 'passed', 'coronavirus', 'school', 'staff', 'members']
['spread', 'covid', 'within', 'schools', 'thus', 'been', 'very', 'limited', 'virus', 'transmission', 'children', 'schools', 'less', 'than', 'that', 'influenza']
['newspaper']
['sydney', 'club', 'shut', 'repeat', 'covid', 'breaches']
['australian', 'associated', 'press']
['published', 'october', '2020', 'updated', 'october', '2020']
['sydney', 'portugal', 'community', 'club', 'being', 'forced', 'shut', 'seven', 'days', 'after', 'failing', 'comply', 'with', 'covid', 'safe', 'regulations', 'even', 'after', 'three', 'warnings', '5000', 'fine']
['liquor', 'gaming', 'director', 'compliance', 'dimitri', 'argeres', 'said', 'portugal', 'club', 'marrickville', 'fourth', 'business', 'closed', 'covid', 'safety', 'breaches']
['will', 'close', 'from', 'wednesday', 'week']
['inspectors', 'visited', 'club', 'three', 'times', 'observed', 'breaches', 'relating', 'their', 'covid', 'safety', 'plan', 'overbooking', 'groups', 'gaming', 'machine', 'spacing', 'inadequate', 'check', 'processes', 'argeres', 'said', 'tuesday']
['since', 'closure', 'order', 'issued', 'however', 'some', 'things', 'club', 'fined', 'longer', 'breaches', 'example', 'bookings', 'people', 'rather', 'than', 'while', 'guests', 'need', 'spaced', 'least', 'metres', 'apart', 'gaming', 'machines']
['what', 'changed', 'having', 'robust', 'digital', 'check', 'processes', 'that', 'allow', 'effective', 'contact', 'tracing', 'event', 'positive', 'case', 'visited', 'venue', 'argeres', 'said']
['service', 'free', 'covid', 'safe', 'check', 'code', 'businesses', 'registered', 'covid', 'safe', 'anyone', 'check', 'using', 'this', 'code', 'even', 'they', 'have', 'service', 'account']
['inspectors', 'from', 'liquor', 'gaming', 'fair', 'trading', 'safework', 'have', 'conducted', '5605', 'covid', 'safety', 'visits', 'issued', 'penalties', 'worth', 'temporarily', 'closed', 'four', 'businesses']
['newspaper']
['tests', 'underway', 'covid', 'treatment']
['australian', 'associated', 'press']
['published', 'august', '2020', 'updated', 'august', '2020']
['australian', 'researchers', 'testing', 'treatment', 'coronavirus', 'they', 'hope', 'will', 'provide', 'relief', 'those', 'infected', 'with', 'covid', 'limit', 'spread']
['professor', 'david', 'morris', 'international', 'team', 'have', 'adapted', 'cancer', 'treatment', 'known', 'bromac', 'that', 'could', 'prevent', 'virus', 'moving', 'into', 'lungs', 'carriers', 'spreading', 'other', 'people']
['trial', 'coronavirus', 'patients', 'being', 'established', 'with', 'melbourne', 'hospital', 'could', 'start', 'next', 'month']
['bromac', 'been', 'under', 'development', 'years', 'cancer', 'treatment', 'includes', 'components', 'that', 'together', 'dissolve', 'spike', 'covid', 'rendering', 'unable', 'infect', 'other', 'cells']
['core', 'agents', 'pineapple', 'stem', 'enzyme', 'tested', 'after', 'observed', 'that', 'pigs', 'eating', 'pineapples', 'were', 'resistant', 'particular', 'gastro', 'conditions']
['taken', 'drug', 'development', 'more', 'than', 'decade', 'asked', 'whether', 'adapted', 'treating', 'people', 'infected', 'with', 'covid', 'professor', 'david', 'morris', 'said', 'statement', 'monday']
['results', 'show', 'drug', 'renders', 'covid', 'spike', 'ineffective', 'stopping', 'from', 'infecting', 'other', 'cells']
['hope', 'results', 'will', 'show', 'treatment', 'confine', 'covid', 'nose', 'throat', 'prevent', 'lung', 'infection', 'stop', 'infected', 'patients', 'from', 'passing', 'live', 'virus', 'said']
['hoped', 'infected', 'person', 'could', 'take', 'medication', 'first', 'signs', 'covid', 'stop', 'virus', 'tracks']
['this', 'could', 'provide', 'very', 'safe', 'effective', 'saving', 'lives', 'protecting', 'vulnerable', 'workers', 'preventing', 'hospitalisation', 'getting', 'people', 'infected', 'covid', 'returning', 'back', 'their', 'everyday', 'lives']
['researchers', 'stress', 'treatment', 'vaccine', 'covid', 'could', 'work', 'tandem', 'treatment', 'prevention', 'measure']

# LSA Model
number_of_topics=100
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values

#top 5 words from lsa
dict(model_nscience.show_topic(0, topn=5))

{'trial': -0.42977519738264497,
 'vaccin': -0.3531586004468163,
 'stage': -0.3053952578874054,
 'late': -0.2756054075105182,
 'expect': -0.21725840938344546}

#word cloud for top 5 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph11.png')

# cosine similarity of top 5 words


a=pd.DataFrame()

for i in range(number_of_topics):
    words_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled13.png')

cosine-similarity
            trial     vaccin     stage       late    expect
trial           1  0.0812451  0.884778   0.710032  0.642674
vaccin  0.0812451          1  0.127836  0.0928863  0.029285
stage    0.884778   0.127836         1   0.782065  0.659594
late     0.710032  0.0928863  0.782065          1  0.553493
expect   0.642674   0.029285  0.659594   0.553493         1

#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig13.png')

LSA on covid-psychology-small.txt at paragraph level

# data loading
document_list,titles,word_count_psy=load_data("","covid-psychology.txt")

Number of words in text file : 86550
Total Number of Documents: 9007

#data cleaning
clean_text=preprocess_data(document_list)

['international', 'sociology']
['2021']
['author', '2020']
['article', 'reuse', 'guidelines']
['sagepub', 'journals', 'permissions']
['1177', '0268580920948807']
['journals', 'sagepub', 'home']
['psychology', 'politics']
['covid', 'misinfodemics']
['people']
['believe', 'misinfodemics']
['sonia', 'mukhtar']
['university', 'management', 'technology', 'lahore', 'pakistan']
['abstract']
['misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse']
['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate']
['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects']
['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier']
['about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this']
['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects']
['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article']
['shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product']
['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive']
['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations']
['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications']
['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health']
['keywords']
['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science']
['mechanisms', 'misinfodemics']
['this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding']
['corresponding', 'author']
['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore']
['punjab', '54770', 'pakistan']
['email', 'sonia', 'mukhtar12', 'gmail']
['948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar']
['research', 'article2020']
['article']
['international', 'sociology']
['covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus']
['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern']
['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin']
['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during']
['lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication']
['become', 'major', 'source', 'interaction', 'holman', '2020']
['covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges']
['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable']
['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories']
['claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science']
['times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false']
['context', 'manipulated', 'data', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease']
['mukhtar', '2020a', '2020']
['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'have']
['also', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated']
['these', 'rumors', 'have', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'took']
['life', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'have', 'overdosed']
['drug', 'cholorquine', 'after', 'news', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', '2020', '2020', 'there', 'been', 'failure', 'make']
['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed']
['anti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'such', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'cure', 'vaccine', 'covid', 'this']
['also', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'anti', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'have', 'adverse', 'health']
['psychosocial', 'impacts', 'aguilera', '2020', 'rana', '2020a', '2020b', 'these', 'considerations', 'become', 'even', 'more', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe']
['popular', 'opinion', 'spend', 'ever', 'more', 'time', 'social', 'media', 'people', 'trying']
['make', 'sense', 'their', 'changed', 'lives', 'self', 'proclaimed', 'celebrities', 'self', 'identified', 'social']
['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their']
['subjective', 'interpretations', 'events', 'this', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', '2020']
['scientific', 'studies', 'from', 'previous', 'disease', 'outbreaks', 'have', 'demonstrated', 'that', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', '2019', 'kalichman', '2009', 'individuals', 'endorse']
['misinformation', 'about', 'disease', 'less', 'likely', 'follow', 'public', 'health', 'instructions', 'during']
['ebola', 'outbreak', '2014', 'respondents', 'gave', 'credence', 'conspiracy', 'theories']
['alleged', 'that', 'they', 'would', 'less', 'likely', 'seek', 'support', 'during', 'disease', 'outbreak']
['national', 'cancer', 'institute', '2020', '2019', 'study', 'montanaro', '2020', 'conducted']
['over', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', '2020', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information']
['about', 'covid', 'from', 'current', 'administration', 'mainstream', 'media', 'news', 'outlets']
['york', 'times', '2020']
['mukhtar']
['psychology', 'misinfodemics']
['fear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual']
['perceived', 'threat', 'mertens', '2018', 'however', 'when', 'fear', 'excessive', 'then', 'this']
['have', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'like', 'anxiety']
['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'when', 'fear']
['insufficient', 'then', 'this', 'also', 'cause', 'harm', 'both', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation']
['policies', 'that', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal']
['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'fear', 'fear', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial']
['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'help', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'have', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact']
['efforts', 'curb', 'misinfodemics', 'process', 'have', 'increased', 'because', 'adverse']
['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable']
['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health']
['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination']
['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', '2018', 'propensity', 'follow', 'popular']
['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety', 'fear', 'lead']
['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'high', 'levels', 'uncertainty', 'inability', 'cope', 'with', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety']
['fear', 'health', 'related', 'worry', 'this', 'situation', 'exacerbated', 'exposure', 'plethora']
['information', 'including', 'misinformation', 'disinformation', 'about', 'impending']
['threat', 'from', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', '2019', 'perceived', 'threat']
['information', 'elevates', 'fear', 'repeated', 'engagement', 'with', 'trauma', 'related', 'media', 'content']
['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently']
['will', 'either', 'increase', 'fear', 'virus', 'cause', 'insensitivity', 'towards', 'course']
['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter']
['accuracy', 'content', 'become', 'more', 'challenging', 'increasing', 'number', 'people', 'rely']
['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'they', 'emulsify', 'into', 'false']
['misleading', 'click', 'bait', 'content', 'world', 'health', 'organization', 'launched']
['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic']
['news', 'social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher']
['acute', 'stress', 'holman', '2020', 'media', 'exposure', 'accumulates', 'ever', 'emerging', 'threats']
['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation']
['herd', 'mentality', 'misplaced', 'conviction', 'make', 'people', 'more', 'vulnerable', 'media', 'exposure', 'distress']
['alongside', 'amount', 'media', 'exposure', 'type', 'content', 'exposure', 'matters']
['well', 'exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could']
['instigate', 'posttraumatic', 'stress', 'fear', 'future', 'which', 'will', 'lead', 'poor', 'personal']
['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media']
['have', 'established', 'themselves', 'beyond', 'government', 'channel', 'global']
['international', 'sociology']
['population', 'developed', 'affinity', 'news', 'channels', 'particular', 'while', 'older']
['population', 'mainly', 'relies', 'more', 'traditional', 'news', 'channels', 'youth', 'look', 'social', 'media']
['such', 'tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media']
['have', 'become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followers']
['attract', 'attention', 'create', 'panic', 'liang', '2020', 'some', 'self', 'proclaimed', 'celebrities', 'media', 'stars', 'have', 'become', 'influential', 'that', 'large', 'number', 'people', 'rely', 'solely']
['their', 'content', 'merchant', '2020', 'some', 'social', 'media', 'posts', 'circulating', 'many']
['platforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimal']
['personal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'this', 'traumatic', 'event', 'certain', 'posts', 'like', 'come']
['this', 'quarantine', 'with', 'skill', 'your', 'side', 'hustle', 'started', 'more', 'knowledge', 'then']
['never', 'lacked', 'time', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts']
['prompting', 'people', 'utilize', 'their', 'time', 'learning', 'things', 'skills', 'exacerbate']
['worry', 'already', 'anxiety', 'prone', 'individuals', 'place', 'psychological', 'pressure', 'productive', 'rather', 'messages', 'like', 'come', 'this', 'quarantine', 'with', 'skill']
['your', 'side', 'hustle', 'started', 'more', 'knowledge', 'then', 'doing', 'just', 'fine', 'should', 'disseminated', 'social', 'media', 'individuals', 'realize', 'that', 'during', 'such', 'intense', 'traumatic', 'event', 'everyone', 'equally', 'endowed', 'with', 'strength', 'coping', 'strategies']
['problem', 'solving', 'skills', 'transform', 'trauma', 'into', 'positive', 'emotion', 'which', 'okay']
['source', 'shame', 'guilt']
['there', 'coercive', 'impression', 'percolating', 'among', 'individuals', 'regarding', 'lockdown']
['that', 'people', 'should', 'assume', 'holiday', 'that', 'they', 'must', 'utilize', 'their', 'time', 'productively', 'engage', 'occupational', 'academic', 'activities', 'this', 'psychological', 'pressure']
['further', 'aggravated', 'feelings', 'guilt', 'shame', 'regret', 'sadness', 'self', 'pity', 'anger', 'internalized']
['emotions', 'being', 'overwhelmed', 'negative', 'self', 'talk', 'unrealistic', 'expectations', 'perceived', 'sense', 'failure', 'mukhtar', '2020a', '2020b', 'psychological', 'pressure', 'compete', 'with', 'peers', 'achieving', 'maximum', 'tasks', 'producing', 'occupational', 'academic']
['outcomes', 'harnessing', 'herd', 'followers', 'subscribers', 'media', 'accounts']
['downplaying', 'trauma', 'that', 'others', 'will', 'have', 'devastating', 'effects', 'mental']
['wellbeing']
['lockdown', 'resulting', 'self', 'isolation', 'quarantine', 'social', 'distancing', 'removed']
['from', 'normal', 'leisure', 'time', 'that', 'might', 'utilized', 'improved', 'personal', 'functioning']
['collective', 'traumatic', 'event', 'which', 'poses', 'serious', 'threat', 'people', 'resulted', 'huge']
['loss', 'lives', 'displacement', 'many', 'individuals', 'mukhtar', 'mukhtar', '2020', 'mukhtar']
['rana', '2020', 'covid', 'individual', 'collective', 'traumatic', 'event', 'directly']
['indirectly', 'affected', 'every', 'individual', 'world', 'efforts', 'should', 'directed', 'towards']
['minimizing', 'negative', 'effects', 'this', 'traumatic', 'covid', 'pandemic', 'survivors']
['many', 'people', 'going', 'through', 'interpersonal', 'traumatic', 'events', 'addition', 'collective']
['trauma', 'covid', 'domestic', 'violence', 'gender', 'based', 'violence', 'abuse', 'mukhtar', '2020d']
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['explanation', 'subjective', 'certainty', 'existential', 'desire', 'control', 'security']
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['camps', 'losing', 'sense', 'proportion', 'ability', 'mitigate', 'fear', 'reasonable']
['knowledge', 'based', 'measures', 'challenge', 'normalize', 'perceived', 'threat', 'infection']
['mechanism', 'misinfodemics', 'engrossed', 'silos', 'public', 'information', 'which']
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['mukhtar', '2020e', 'rowlands', '2015']
['politics', 'coronavirus']
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['agenda', 'wage', 'economic', 'psychological', 'chinese', 'biological', 'weapon']
['conspiracy', 'theory', 'which', 'spread', 'throughout', 'united', 'states', 'united', 'kingdom']
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['immigrants', 'mccarthy', '2020']
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['frameworks', 'governments', 'thus', 'such', 'obstinate', 'prejudice', 'confrontations']
['ignite', 'globalization', 'nationalism', 'nativism', 'protectionism', 'tariffs', 'closed', 'borders']
['erection', 'walls', 'which', 'will', 'intensify', 'covid', 'pandemic', 'outbreak']
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['infect', 'anyone', 'since', 'sunnah', 'samaa', '2020', 'claims', 'circulating', 'about', 'pakistanis']
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['measure', 'hospitalized', 'people', 'anti', 'coronavirus', 'drug', 'anti', 'coronavirus']
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['that', 'home', 'remedies', 'cure', 'prevent', 'people', 'from', 'contracting', 'coronavirus']
['these', 'rumored', 'remedies', 'that', 'gained', 'traction', 'social', 'media', 'with', 'severe', 'adverse']
['effects', 'involves', 'mixing', 'sodium', 'chlorite', 'solution', 'with', 'citric', 'acid', 'producing', 'chlorine']
['mukhtar']
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['knowledge', 'challenge', 'these', 'predispositions', 'their', 'effects', 'subsequent', 'belief', 'system']
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['take', 'threat', 'less', 'seriously', 'media', 'outlets', 'including', 'news', 'personalities', 'media', 'figures']
['cast', 'aspersion', 'threat', 'covid', 'questioning', 'hospitals', 'were', 'truly', 'filled', 'with']
['coronavirus', 'infected', 'patients', 'peters', '2020', 'after', 'toll', 'human', 'life', 'became', 'increasingly', 'apparent', 'unassailable', 'change', 'reporting', 'behavior', 'part', 'media']
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['asia', 'chinese', 'coronavirus', 'kung', 'which', 'caused', 'xenophobia', 'misperceptions']
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['japan', 'south', 'korea', 'vietnam', 'indonesia', 'amnesty', 'international', '2020']
['office', 'united', 'nations', 'high', 'commissioner', 'human', 'rights', 'ohchr']
['issued', 'statement', 'twitter', 'understandable', 'alarmed', 'coronavirus']
['amount', 'fear', 'excuse', 'prejudice', 'discrimination', 'against', 'people', 'asian', 'descent']
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['emergency', 'standup4humanrights']
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['physical', 'mental', 'health', 'social', 'interpersonal', 'factors', 'including', 'intimate', 'partner']
['violence', 'marital', 'rape', 'child', 'abuse', 'domestic', 'violence', 'racism', 'xenophobia', 'dissociation']
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['allowed', 'such', 'disease', 'emerge', 'only', 'through', 'rigorous', 'devotion', 'congregational']
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['evil', 'bent', 'destroying', 'faith', 'this', 'dogmatic', 'doctrine', 'seeps', 'through', 'ever', 'present']
['ever', 'omniscient', 'online', 'media', 'infiltrate', 'minds', 'sheeple', 'votary', 'conformists', 'usually', 'heads', 'household', 'enforce', 'these', 'maladaptive', 'cognitive', 'twisted', 'patterns']
['within', 'their', 'families', 'these', 'instructions', 'dictated', 'higher', 'authorities', 'their']
['adherence', 'infringing', 'individual', 'rights', 'freedom', 'planting', 'unquestionable']
['canon', 'propagating', 'anti', 'science', 'medical', 'mistrust', 'among', 'public', 'putting', 'people']
['mukhtar']
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['pandemic', 'thus', 'creating', 'public', 'panic', 'social', 'unrest', 'distrust', 'systems', 'kalb']
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['health', 'related', 'topics', 'widespread', 'prompting', 'people', 'eschew', 'appropriate', 'healthrelated', 'behaviors', 'jolley', 'douglas', '2014', 'oliver', 'wood', '2014']
['potential', 'strategies', 'curtailment']
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['people', 'self', 'isolation', 'social', 'distancing', 'quarantine', 'this', 'crisis', 'situation', 'will']
['require', 'remediation', 'from', 'credible', 'sources', 'information', 'these', 'include']
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['health', 'especially', 'which', 'partnered', 'with', 'several', 'social', 'media']
['platforms', 'technological', 'companies', 'google', 'linkedin', 'microsoft', 'reddit', 'twitter']
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['these', 'efforts', 'infodemics', 'rampant', 'multiple', 'misinformation', 'disinformation']
['sources', 'circulating', 'social', 'media', 'accounts', 'address', 'these', 'discrepancies', 'certain']
['strategies', 'implemented', 'empirically', 'evidence', 'based', 'scientific', 'research', 'findings']
['with', 'integration', 'communication', 'information', 'technology', 'frontline', 'healthcare', 'providers', 'communicate', 'with', 'patients', 'caregivers', 'populations', 'risk']
['results', 'positive', 'health', 'outcomes', 'subsequent', 'optimization', 'resources']
['building', 'strategic', 'partnerships', 'local', 'global', 'levels', 'coordinate', 'connecting', 'offline']
['online', 'resources', 'communication', 'uniform', 'information', 'across', 'platforms', 'from', 'mass', 'media', 'community', 'organizations', 'support', 'groups', 'community']
['society', 'contain', 'infodemics', 'information', 'disinformation', 'disseminate']
['scientifically', 'evidence', 'based', 'information', 'through', 'data', 'mining', 'algorithms', 'detect']
['remove', 'fake', 'news', '2017', 'those', 'propagate', 'misinformation']
['accountable', 'individuals', 'online', 'portals', 'should', 'identified', 'local', 'authorities']
['enforcement', 'agencies', 'precautionary', 'culturally', 'tailored', 'information', 'translated', 'into']
['multiple', 'language', 'manuals', 'factual', 'data', 'regarding', 'covid', 'should', 'promoted']
['through', 'mass', 'media', 'campaigns', 'care', 'evidence', 'based', 'approach', 'services']
['local', 'languages', 'mostly', 'graphic', 'pictorial', 'easy', 'understanding', 'should', 'made']
['available', 'people', 'with', 'limited', 'access', 'healthcare', 'elderly', 'people', 'rural', 'areas']
['general', 'public', 'self', 'isolation', 'quarantined', 'decrease', 'covid', 'infection']
['imperative', 'bring', 'communities', 'institutional', 'leadership', 'together', 'promote']
['transparency', 'good', 'information', 'governance', 'control', 'misinfodemics', 'related']
['covid', 'both', 'people', 'physical', 'mental', 'psychosocial', 'health', 'sound', 'economic', 'political', 'systematic', 'functioning', 'during', 'this', 'pandemic', 'oliver', 'wood', '2014']
['world', 'needs', 'coordinated', 'national', 'international', 'efforts', 'apply', 'scientific']
['empirical', 'data', 'local', 'settings', 'mitigate', 'grave', 'predicament', 'lockdown', 'aftereffects']
['international', 'sociology']
['associated', 'issues', 'mass', 'media', 'healthcare', 'organizations', 'community', 'based', 'organizations', 'stakeholders', 'should', 'strategically', 'join', 'partnership', 'disseminate', 'based']
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['online', 'content', 'through', 'natural', 'language', 'processing', 'data', 'mining', 'approaches', 'best']
['global', 'solution', 'global', 'disease', 'international', 'collaboration', 'exchange', 'scientific', 'ideas', 'health', 'communication', 'facilitating', 'coordination']
['paper']
['impact', 'covid', 'psychology', 'among', 'university']
['students']
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['purpose', 'study', 'find', 'psychological', 'impact']
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['health', 'university', 'students']
['introduction']
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['according']
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['data', 'bangladesh', 'workers', 'earn']
['more', 'than', 'taka']
['economic', 'shutdown', 'sparked', 'covid']
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['serve', 'each', 'high', 'performance', 'athlete']
['international', 'journal', 'sport', 'exercise', 'psychology', '271paths', 'ahead', 'tokyo', '2021']
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['intermediate', 'steps', 'visible', 'culmination', 'current', 'olympic', 'quadrennia']
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['modified', 'strengthened', 'athletes', 'enhanced', 'service', 'provisions', 'moments', 'quietness']
['present', 'openings', 'reflect', 'evaluate', 'revise', 'reform', 'plans', 'these', 'plans', 'will', 'undoubtedly']
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['built', 'result', 'what', 'could', 'easily', 'dismissed', 'isolated', 'unfortunate', 'moment']
['human', 'also', 'olympic', 'history', 'however', 'cannot', 'underestimate', 'strength']
['human', 'spirit', 'olympians', 'they', 'will', 'continue', 'forged', 'through']
['aversities']
['paper']
['covid', 'school', 'psychology', 'adaptations', 'directions', 'field']
['samuel', 'songa']
['cixin', 'wangb', 'dorothy', 'espelagec']
['fenningd', 'shane', 'jimersone']
[]
['university', 'nevada', 'vegas', 'buniversity', 'maryland']
['university', 'north', 'carolina', 'chapel', 'hill', 'dloyola', 'university', 'chicago']
['university']
['california', 'santa', 'barbara']
['abstract']
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[]
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['riphah', 'international', 'university', 'lahore', 'pakistan']
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['mobilize', 'study', 'rapid', 'evaluation', 'treatment', 'community', 'level']
['international', 'journal', 'business', 'psychology']
['issue', '1ijbpsy', 'nawaz', '2020']
[]
['evaluate', 'available', 'data', 'immediately', 'find', 'what', 'level', 'treatment', 'strategies']
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['evaluate', 'impact', 'adjunctive', 'supportive', 'therapies', 'fast', 'possible']
['optimize', 'protective', 'equipment', 'other', 'interventions', 'avoid', 'manage']
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[]
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[]
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['italy']
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[]
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[]
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[]
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[]
[]
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[]
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['service', 'industries', 'financial', 'markets']
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[]
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['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
['1usd']
[]
['sprt', 'γ2ausc']
['γ2busc']
['γ3ausd']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['γ1nsrt', 'γ2ausc']
['γ2busc']
[]
[]
['γ3busd']
[]
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spd']
[]
[]
[]
[]
[]
[]
['γ1ibert', 'γ2aspc']
['γ2bspc']
[]
[]
['γ3bspd']
[]
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
['itld']
['γ1ftmrt', 'γ2aitlc']
[]
['itlc']
[]
['itld']
[]
['itld']
[]
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
['gerd']
['γ1daxrt', 'γ2agerc']
['γ2bgerc']
[]
['gerd']
['γ3bgerd']
[]
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukd']
[]
[]
[]
[]
[]
[]
['γ1lsert', 'γ2aukc']
['γ2bukc']
[]
[]
['γ3bukd']
[]
['here', 'ϑ0ϑ1', 'ϑ2bϑ3aand', 'short', 'factors', 'while']
[]
[]
['indicates', 'long', 'parameters']
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['based', 'akaik', 'information', 'criterion', 'schwarz', 'information']
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['asymmetrical', 'impact', 'stock', 'returns']
['thus']
[]
[]
[]
[]
[]
[]
[]
['itlc']
[]
[]
['itld']
[]
['gerc']
[]
['gerd']
[]
[]
[]
[]
[]
['designates', 'positive', 'shock', 'though']
[]
[]
[]
[]
[]
[]
[]
[]
['itlc']
[]
['itld']
[]
['gerc']
[]
['gerd']
[]
[]
[]
[]
[]
[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['here']
[]
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['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
['itld']
['φ1ectt']
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
['gerd']
['φ1ectt']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['andshort']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['asymmetric']
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[]
[]
['daxr']
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[]
[]
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[]
[]
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['support', 'giving', 'emotional', 'practical', 'companionship', 'fiske', '2007', 'power', 'expressions', 'that']
['mark', 'person', 'power', 'over', 'behavior', 'outcomes', 'another', 'blau', '1964']
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['threshold', 'based', 'indicator', 'function']
[]
[]
[]
[]
[]
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['peak']
['keywords', 'supporting', 'literature', 'lang', 'categories']
[]
[]
['fear']
['emotional', 'maelstrom', 'swear', 'liwc']
['anger', 'liwc']
['negemo', 'liwc']
['these', 'liwc', 'categories', 'have', 'been', 'used', 'analyze']
['complex', 'emotional', 'responses', 'traumatic', 'events']
['ptsd', 'characterize', 'language', 'people']
['suffering', 'from', 'mental', 'health', 'coppersmith', '2014']
['sadness', 'liwc']
['fear', 'fear', 'related', 'words', 'like', 'ones', 'included', 'emolex', 'have']
['been', 'often', 'used', 'measure', 'fear', 'both', 'tangible']
['intangible', 'threats', 'kahn', '2007', 'gill', '2008']
['fear', 'emolex']
['death', 'liwc']
['anxiety', 'panic', 'anxiety', 'category', 'liwc', 'been', 'used', 'study', 'ferent', 'forms', 'anxiety', 'social', 'media', 'shen', 'rudzicz']
['2017']
['anxiety', 'liwc']
['disorientation', 'definition', 'tentative', 'category', 'liwc', 'expresses']
['uncertainty', 'tausczik', 'pennebaker', '2010']
['tentative', 'liwc']
['suspicion', 'suspicion', 'often', 'formalized', 'lack', 'trust', 'deutsch']
['1958']
['trust', 'liwc']
['irrationality', 'negate', 'category', 'liwc', 'been', 'used', 'measure']
['cognitive', 'distorsions', 'irrational', 'interpretations', 'ality', 'simms', '2017']
['negate', 'liwc']
['religion', 'religious', 'expressions', 'from', 'liwc', 'have', 'been', 'used', 'study']
['people', 'appeal', 'religious', 'entities', 'during', 'moments']
['hardship', 'shaw', '2007']
['religion', 'liwc']
['contagion', 'these', 'liwc', 'categories', 'were', 'used', 'study']
['perception', 'diseases', 'cancer', 'support', 'groups', 'people']
['affected', 'eating', 'disorder', 'alcoholics', 'alpers']
['2005', 'wolf', '2013', 'kornfield', '2018']
['body', 'liwc']
['feel', 'liwc']
['moralization']
['warn', 'risk', 'avoidance']
['risk', 'perception']
['liwc', 'category', 'used', 'model', 'risk', 'perception', 'connected']
['epidemics', '2020']
['risk', 'liwc']
['polarization', 'segregation', 'different', 'personal', 'pronouns', 'have', 'been', 'used', 'study', 'group', 'dynamics', 'characterize', 'language']
['markers', 'racism', 'arguello', '2006', 'personal']
['pronouns', 'markers', 'differentiation', 'have', 'been']
['considered', 'studies', 'racist', 'language', 'figea']
['2016']
['liwc']
['liwc']
['they', 'liwc']
['differ', 'liwc']
['stigmatization', 'blame']
['abuse']
['pronouns', 'they', 'were', 'used', 'quantify', 'blame']
['personal', 'borelli', 'sbarra', '2011', 'political', 'text', 'windsor', '2014', 'hate', 'speech', 'associated', 'with']
['they', 'oriented', 'statements', 'elsherief', '2018']
['same', 'categories']
['previous', 'line']
['cooperation']
['coordination', 'collective']
['consciousness']
['moral', 'value', 'care', 'expresses', 'will', 'protecting']
['others', 'graham', '2009', 'cooperation', 'often']
['verbalized', 'referencing', 'group', 'expressing']
['affiliation', 'rezapour', '2019']
['affiliation', 'liwc']
['care', 'moral', 'virtue']
['prosocial', 'prosocial']
['faith', 'authority', 'moral', 'value', 'authority', 'expresses', 'will', 'playing']
['rules', 'hierarchy', 'versus', 'challenging', 'rezapour']
['2019']
['authority', 'moral']
['virtue']
[]
['authority', 'enforcement', 'power', 'category', 'liwc', 'expresses', 'exertion', 'inance', 'tausczik', 'pennebaker', '2010']
['power', 'liwc']
['action']
['restrictions', 'travel', 'vacy']
['motion', 'liwc']
['home', 'liwc']
['work', 'liwc']
['social', 'liwc']
['daily', 'habits', 'concern', 'mainly', 'people', 'experience']
['home', 'work', 'leisure', 'movement', 'between']
['them', 'gonzalez', '2008']
['leisure', 'liwc']
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['relevant', 'language', 'categories', 'from', 'existing', 'language', 'lexicons', 'used', 'psychometric', 'studies', 'category', 'names']
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['days', 'when', 'their', 'standardized', 'fractions', 'reached', 'maximum', 'reported', 'percentage', 'increase', 'peak']
['compared', 'average', 'over', 'whole', 'time', 'period', 'each', 'maximum', 'value', 'highlighted', 'bold', 'epidemic', 'psychology', 'works', 'twitter']
['constructed', 'temporal', 'signals', 'language', 'categories', 'counted', 'number', 'users', 'posted']
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[]
[]
[]
['last', 'normalized', 'these', 'fractions', 'considering', 'minimum', 'maximum', 'values', 'during', 'whole', 'time']
['period']
[]
[]
[]
['mentions', 'medical', 'entities', 'used', 'state', 'deep', 'learning', 'method', 'medical', 'entity', 'extraction']
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['method', 'extracts', 'grams', 'representing', 'medical', 'symptoms', 'feeling', 'sick', 'this', 'method', 'based']
['lstm', 'sequence', 'tagging', 'architecture', 'huang', '2015', 'combination', 'with', 'glove', 'word', 'embeddings', 'nington', '2014', 'roberta', 'contextual', 'embeddings', '2019', 'optimize', 'entity', 'extraction']
['performance', 'noisy', 'textual', 'data', 'from', 'social', 'media', 'trained', 'sequence', 'tagging', 'architecture', 'micromed']
['database', 'jimeno', 'yepes', '2015', 'collection', 'tweets', 'manually', 'labeled', 'with', 'medical', 'entities', 'hyper', 'parameters', 'used', 'hidden', 'units', 'batch', 'size', 'learning', 'rate', 'which', 'gradually', 'halved']
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['before', 'learning', 'rate', 'became', 'small', '0001', 'final', 'model', 'achieved', 'score', 'micromed']
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['medical', 'entities', 'recall', 'fraction', 'medical', 'entities', 'present', 'text', 'that', 'method', 'able', 'retrieve']
['based', 'implementation', 'flair', 'akbik', '2019', 'pytorch', 'paszke', '2017', 'popular', 'deep']
['learning', 'libraries', 'python']
['each', 'unique', 'medical', 'entity', 'counted', 'number', 'users', 'posted', 'least', 'tweet', 'time']
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['total', 'number', 'users', 'tweeted', 'time']
[]
[]
[]
['last', 'normalize', 'these', 'fractions', 'considering', 'minimum', 'maximum', 'values', 'during', 'whole', 'time']
['period']
[]
[]
[]
['mobility', 'traces', 'foursquare', 'local', 'search', 'discovery', 'mobile', 'application', 'that', 'relies', 'users', 'past', 'mobility']
['records', 'recommend', 'places', 'user', 'might', 'like', 'application', 'uses', 'localization', 'estimate']
['user', 'position', 'infer', 'places', 'they', 'visited', 'response', 'covid', 'crisis', 'foursquare', 'made', 'publicly']
['available', 'data', 'gathered', 'from', 'pool', 'million', 'users', 'these', 'users', 'were', 'always', 'during', 'period']
['data', 'collection', 'meaning', 'that', 'they', 'allowed', 'application', 'gather', 'location', 'data', 'times', 'even', 'when']
['application', 'data', 'published', 'through', 'visitdata', 'website', 'consists', 'daily', 'number']
['users', 'visiting', 'venue', 'type', 'state', 'starting', 'from', 'february', 'present']
['users', 'visited', 'schools', 'indiana', 'february', 'overall', 'distinct', 'location', 'categories', 'were', 'provided', 'obtain']
['country', 'wide', 'temporal', 'indicators', 'first', 'applied', 'normalization', 'values']
[]
[]
[]
['then', 'averaged', 'values', 'across', 'states']
[]
[]
[]
[]
[]
['where', 'total', 'number', 'states', 'weighting', 'each', 'state', 'equally', 'obtained', 'measure', 'that', 'more']
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['time', 'series', 'smoothing', 'temporal', 'indicators', 'affected', 'large', 'fluctuations', 'extract']
['more', 'consistent', 'trends', 'time', 'series', 'applied', 'smoothing', 'function', 'common', 'practice', 'when', 'analyzing']
['temporal', 'data', 'extracted', 'from', 'social', 'media', 'connor', '2010', 'given', 'time', 'varying', 'signal', 'apply']
['boxcar', 'moving', 'average', 'over', 'window', 'previous', 'days']
[]
[]
[]
[]
[]
[]
[]
[]
['selected', 'window', 'week', 'weekly', 'time', 'windows', 'typically', 'used', 'smooth', 'both']
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['language', 'categories']
[]
[]
['mentions', 'medical', 'entities']
[]
[]
['interaction', 'types']
[]
[]
[]
['foursquare', 'visits']
[]
[]
[]
['change', 'point', 'detection', 'identify', 'phases', 'characterized', 'different', 'combinations', 'language', 'categories']
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[]
[]
['namely', 'daily', 'average', 'squared', 'gradi', 'utkepohl', '2005', 'smoothed', 'standardized', 'fractions', 'that', 'category', 'calculate', 'gradient', 'used']
['python', 'function', 'numpy', 'gradient', 'gradient', 'provides', 'measure', 'rate', 'increase', 'decrease']
['signal', 'consider', 'absolute', 'value', 'gradient', 'account', 'magnitude', 'change', 'rather', 'than', 'direc', 'tion', 'change', 'identify', 'periods', 'consistent', 'change', 'opposed', 'quick', 'instantaneous', 'shifts', 'apply', 'temporal']
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[]
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[]
[]
[]
[]
[]
[]
[]
[]
[]
['peaks', 'time', 'series', 'represent', 'days', 'highest', 'variation', 'marked', 'them', 'change', 'points', 'using']
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['than', 'average', 'plus', 'standard', 'deviations', 'common', 'practice', 'palshikar', '2009']
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['pandemic', 'entailed', 'first', 'time', 'going', 'through', 'entire', 'life', 'cycle', 'making', 'this', 'contagion', 'wave']
['self', 'contained', 'arrival', 'unknown', 'virus', 'skepticism', 'isolation', 'measures', 'full', 'lock', 'down', 'first', 'reopening']
['figure', 'shows', 'standardized', 'fractions', 'language', 'categories', 'formula', 'changed', 'from']
['february', 'april', '15th', 'which', 'restrictions', 'most', 'states', 'were', 'lifted', 'cell', 'color', 'encodes', 'values']
['higher', 'than', 'average', 'lower', 'blue', 'partitioned', 'language', 'categories', 'according', 'three']
['psycho', 'social', 'epidemics', 'figure', 'shows', 'value', 'average', 'squared', 'gradient', 'over', 'time', 'formula']
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[]
['announcement', 'first', 'infection', 'country', 'march', '24th', 'announcement']
['stay', 'home', 'orders', 'these', 'change', 'points', 'identify', 'three', 'phases', 'which', 'described', 'next', 'dwelling']
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[]
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['expressions', 'concrete', 'health', 'concerns', 'such', 'words', 'expressing', 'risk', 'mentions', 'body', 'parts', 'feel']
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['affected', 'impending', 'restriction', 'policies', 'such', 'motion', 'social', 'activities', 'leisure', 'mentions', 'words8', 'epidemic', 'psychology', 'works', 'twitter']
['fear']
['death']
['anxiety']
['negemo']
['anger']
['swear']
['tentative']
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['body']
['feel']
['religion']
['posemo']
['sadness']
['trust']
['fear']
['refusal', 'phase', 'anger', 'phase', 'acceptance', 'phase']
[]
['they']
['risk']
['differ']
[]
[]
['affiliation']
['authority']
['power']
['care']
['prosocial']
['morality']
['they', 'focus', 'focus', 'focus']
[]
['motion']
['home']
['social']
['leisure']
['action']
['work']
['usual', 'home', 'beyond', 'home']
[]
[]
['gradient']
[]
[]
[]
[]
['custom', 'words']
[]
['alcohol', 'exercising', 'economic']
[]
[]
[]
['interactions']
[]
['support', 'power', 'conflict']
[]
[]
[]
['medical']
[]
['physical', 'health', 'mental', 'health']
['2020']
[]
[]
[]
['mobility']
[]
['first', 'case']
[]
['stay', 'home']
[]
['grocery', 'travel', 'transport', 'outdoors', 'recreation']
[]
[]
[]
['2020', '2020', '2020', '2020', '2020']
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['localization', 'service', 'foursquare', 'mobile', 'timeline', 'bottom', 'figure', 'marks', 'some']
['events', 'covid', 'pandemic', 'such', 'announcements', 'first', 'infection', 'covid']
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['related', 'home', 'peaked', 'march', '16th', 'when', 'federal', 'government', 'announced', 'social', 'distancing']
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[]
[]
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[]
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[]
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[]
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[]
[]
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[]
[]
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[]
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['cancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer']
['2007']
['shen', 'rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'from', 'linguistic', 'signal', 'clinical', 'reality', 'pages', '2017']
['shultz', 'baingana', 'neria', '2014', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response', 'jama', '2015']
['simms', 'ramstedt', 'rich', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortions']
['through', 'machine', 'learning', 'text', 'analytics', '2017', 'ieee', 'international', 'conference', 'healthcare', 'informatics', 'ichi']
['pages', 'ieee', '2017']
['smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association']
['2012']
['sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'fake', 'news', 'exploring']
['health', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'case', 'study', 'american', 'journal']
['health', 'education', '2018']
['strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness', '1990']
['tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizational']
['identity', '1979']
['tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'liwc', 'computerized', 'text', 'analysis']
['methods', 'journal', 'language', 'social', 'psychology', '2010', 'epidemic', 'psychology', 'works', 'twitter']
['economist', 'with', 'millions', 'stuck', 'home', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international', '2020']
['with', 'millions', 'stuck', 'home', 'online', 'wellness', 'industry', 'booming']
['bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett', 'crum']
['douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response']
['nature', 'human', 'behaviour', 'pages', '2020']
['varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection']
['estimation', 'characterization', 'eleventh', 'international', 'aaai', 'conference', 'social', 'media', '2017']
['wang', 'immediate', 'psychological', 'responses', 'associated']
['factors', 'during', 'initial', 'stage', '2019', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'population']
['china', 'international', 'journal', 'environmental', 'research', 'public', 'health', '1729', '2020']
['waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion']
['comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society', '1813', '1831', '2018']
['windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises']
['risk', 'hazards', 'crisis', 'public', 'policy', '2014']
['wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center', '2019']
['wolf', 'theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'online']
['activity', 'journal', 'language', 'social', 'psychology', '2013']
['wood', 'propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', '2015', '2016', 'zika', 'virus', 'outbreak']
['cyberpsychology', 'behavior', 'social', 'networking', '2018']
['yang', 'torres', 'lugo', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during']
['covid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '2004', '14484', '2020']
['acknowledgments']
['thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'this', 'research']
['partly', 'supported', 'grant', 'green', 'routes', '869764']
['data', 'availability']
['daily', 'aggregates', 'measurements', 'available', 'https', '6084', 'figshare', '14892642']
['tweet', 'used', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasets']
['visualizations', 'available', 'project', 'site', 'http', 'social', 'dynamics', 'epidemicpsychology']
['competing', 'interests']
['author', 'declare', 'competing', 'interests', 'epidemic', 'psychology', 'works', 'twitter']
['supplementary', 'materials']
['spatial', 'representativeness', 'twitter', 'data', 'make', 'sure', 'that', 'geographical', 'penetration', 'twitter']
['data', 'reflects', 'spatial', 'distribution', 'population', 'compared', 'user', 'activity', 'each', 'state', 'with']
['census', 'population', 'figure', 'shows', 'that', 'both', 'number', 'twitter', 'users', 'volume', 'their', 'tweets']
['correlate', 'very', 'strongly', 'with', 'census', 'population', 'population', 'estimates', 'year', '2019', 'level', 'states']
['https', 'census']
[]
['population']
[]
[]
[]
[]
[]
['tweets']
[]
[]
[]
[]
[]
['population']
[]
[]
[]
[]
[]
[]
['users']
[]
[]
[]
[]
['figure', 'correlation', 'census', 'population', 'states', 'estimated', 'year', '2019', 'with']
['number', 'tweets', 'posted', 'those', 'states', 'left', 'number', 'unique', 'twitter', 'users', 'posted']
['least', 'tweet', 'right', 'during', 'period', 'analysis', 'linear', 'shown', 'together', 'with']
['coefficient', 'determination', 'linear', 'regression']
['pearson', 'correlation', 'coefficient']
['coefficient', 'number', 'tweets', 'that', 'twitter', 'users', 'scale', 'almost', 'linearly']
['with', 'population', 'estimates']
['signals', 'racism', 'twitter', 'discussions', 'corroborate', 'qualitative', 'intuition', 'that', 'first', 'phase']
['characterized', 'discussions', 'that', 'depicted', 'pandemic', 'foreign', 'problem', 'often', 'using', 'racist', 'undertones']
['measured', 'volume', 'tweets', 'containing', 'hashtags', 'that', 'were', 'clear', 'indicators', 'content', 'that', 'either', 'racist']
['aimed', 'antagonizing', 'china', 'manually', 'selected', 'those', 'hashtags', 'from', 'list', 'hashtags', 'that', 'appeared']
['least', 'times', 'dataset', 'figure', 'shows', 'normalized', 'volume', 'those', 'hashtags', 'over', 'time', 'they']
['peak', 'during', 'first', 'phase', 'when', 'fear', 'related', 'mentions', 'focus', 'twitter', 'discussions', 'china', 'asian']
['foreigners', 'were', 'their', 'peaks']
['breakdown', 'phases', 'state', 'during', 'period', 'analysis', 'country', 'fragmented', 'into', 'regions', 'that']
['were', 'dealing', 'with', 'different', 'stages', 'outbreak', 'policies', 'contrast', 'varied', 'across', 'states', 'assess']
['whether', 'those', 'differences', 'impacted', 'unfolding', 'psycho', 'social', 'epidemics', 'across', 'states', 'computed']
['measures', 'that', 'represent', 'relative', 'presence', 'three', 'phases', 'over', 'time', 'refusal', 'anger', 'acceptance']
['each', 'state', 'individually', 'results', 'shown', 'figure', 'despite', 'minor', 'differences', 'state', 'level', 'curves']
['indicate', 'that', 'three', 'phases', 'took', 'place', 'states', 'sequence', 'phases', 'consistent', 'across', 'states']
['refusal', 'followed', 'anger', 'which', 'then', 'followed', 'acceptance', 'there', 'very', 'little', 'variation']
['times', 'transition', 'between', 'phases', 'epidemic', 'psychology', 'works', 'twitter']

# LSA Model
number_of_topics=100
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

#top 5 words from lsa
dict(model_psy.show_topic(0, topn=5))

{'2020': 0.744760590312811,
 'covid': 0.2746753833089446,
 'pandem': 0.22338134342717259,
 'health': 0.20627927347916214,
 'polici': 0.15875976238455333}

#word cloud for top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph29.png')

# cosine similarity of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled14.png')

cosine-similarity
               2020        covid       pandem       health      polici
2020              1 -5.71895e-05  0.000716471  0.000112255 -0.00409684
covid  -5.71895e-05            1   0.00019057  -0.00189745  0.00221843
pandem  0.000716471   0.00019057            1  -0.00179825  -0.0134542
health  0.000112255  -0.00189745  -0.00179825            1 -0.00486343
polici  -0.00409684   0.00221843   -0.0134542  -0.00486343           1

#networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig14.png')

mantel_test(5)

Pairwise-distance
            2020        covid    pandem       health    polici      imag  \
2020           0      1.37262   1.03537      1.28802  0.951978   1.36784   
covid    1.37262  1.49012e-08   1.07174      1.32865  0.995743   1.40464   
pandem   1.03537      1.07174         0     0.950417  0.452413   1.09894   
health   1.28802      1.32865  0.950417  2.10734e-08  0.879383    1.3261   
polici  0.951978     0.995743  0.452413     0.879383         0  0.997534   
imag     1.36784      1.40464   1.09894       1.3261  0.997534         0   
use      1.37058      1.40725   1.09596      1.32584    0.9984   1.40638   
data     1.36572      1.40266   1.08629      1.32313  0.987681   1.40569   
model    1.37009      1.40533   1.09521      1.32659  0.998399   1.40384   

            use      data     model  
2020    1.37058   1.36572   1.37009  
covid   1.40725   1.40266   1.40533  
pandem  1.09596   1.08629   1.09521  
health  1.32584   1.32313   1.32659  
polici   0.9984  0.987681  0.998399  
imag    1.40638   1.40569   1.40384  
use           0   1.40355   1.40608  
data    1.40355         0   1.40248  
model   1.40608   1.40248         0  
Pairwise-distance
            2020     covid   pandem       health       polici      imag  \
2020           0   1.40842  1.40475      1.39349      1.19192  0.997544   
covid    1.40842         0   1.4034      1.39314      1.18645  0.995691   
pandem   1.40475    1.4034        0      1.38992      1.19124   0.99091   
health   1.39349   1.39314  1.38992  2.58096e-08      1.17268  0.975351   
polici   1.19192   1.18645  1.19124      1.17268  1.82501e-08  0.651343   
imag    0.997544  0.995691  0.99091     0.975351     0.651343         0   
use      1.37027   1.36815  1.36417      1.35124       1.1333  0.940915   
data     1.36714   1.36384  1.36214      1.34878       1.1427  0.936453   
model    1.33811    1.3362  1.33299      1.31859       1.1196  0.892189   

             use         data        model  
2020     1.37027      1.36714      1.33811  
covid    1.36815      1.36384       1.3362  
pandem   1.36417      1.36214      1.33299  
health   1.35124      1.34878      1.31859  
polici    1.1333       1.1427       1.1196  
imag    0.940915     0.936453     0.892189  
use            0      1.30905      1.27562  
data     1.30905  2.58096e-08      1.29622  
model    1.27562      1.29622  2.10734e-08  

(0.08137012686728233, 0.3607, 0.23074039777307215)

mantel_test(10)

Pairwise-distance
               2020        covid    pandem       health     polici  psycholog  \
2020              0      1.37262   1.03537      1.28802   0.951978    0.94286   
covid       1.37262  1.49012e-08   1.07174      1.32865   0.995743   0.994685   
pandem      1.03537      1.07174         0     0.950417   0.452413   0.459164   
health      1.28802      1.32865  0.950417  2.10734e-08   0.879383   0.876935   
polici     0.951978     0.995743  0.452413     0.879383          0  0.0870882   
psycholog   0.94286     0.994685  0.459164     0.876935  0.0870882          0   
impact     0.948046     0.994606  0.462294     0.841503   0.116237  0.0916567   
countri    0.998861      1.06166  0.495437     0.914789   0.373444   0.359153   
develop     1.07845      1.12368  0.672038      1.05522   0.500555   0.514655   
nation     0.974168      1.02459  0.508374     0.847533   0.256235   0.229322   
imag        1.36784      1.40464   1.09894       1.3261   0.997534   0.992911   
use         1.37058      1.40725   1.09596      1.32584     0.9984   0.995574   
data        1.36572      1.40266   1.08629      1.32313   0.987681   0.989576   
model       1.37009      1.40533   1.09521      1.32659   0.998399   0.993666   
detect      1.34284       1.3742   1.07082      1.29934   0.960186   0.955046   
dataset     1.36678      1.40384   1.09729      1.32913   0.997587   0.992406   
learn       1.34649      1.38262   1.05439      1.30164    0.96464   0.958069   
pneumonia   1.32241      1.35689   1.06773      1.26718   0.925534   0.922983   
case        1.36624      1.40235   1.09547       1.3214   0.985204   0.988768   

                impact      countri   develop    nation      imag       use  \
2020          0.948046     0.998861   1.07845  0.974168   1.36784   1.37058   
covid         0.994606      1.06166   1.12368   1.02459   1.40464   1.40725   
pandem        0.462294     0.495437  0.672038  0.508374   1.09894   1.09596   
health        0.841503     0.914789   1.05522  0.847533    1.3261   1.32584   
polici        0.116237     0.373444  0.500555  0.256235  0.997534    0.9984   
psycholog    0.0916567     0.359153  0.514655  0.229322  0.992911  0.995574   
impact     1.86265e-09     0.355451  0.499189  0.223964  0.999495   1.00256   
countri       0.355451  7.45058e-09  0.508593  0.440892   1.05917   1.06014   
develop       0.499189     0.508593         0  0.518995   1.11847   1.11817   
nation        0.223964     0.440892  0.518995         0   1.01615   1.02501   
imag          0.999495      1.05917   1.11847   1.01615         0   1.40638   
use            1.00256      1.06014   1.11817   1.02501   1.40638         0   
data          0.991414      1.03388   1.12068   1.01515   1.40569   1.40355   
model         0.999246      1.06051   1.11272   1.02071   1.40384   1.40608   
detect        0.959072      1.01322   1.07797  0.988459   1.38148   1.37941   
dataset       0.996151      1.05761   1.11354   1.01716   1.40572   1.40489   
learn          0.95205      1.02136   1.07576  0.989117   1.38078    1.3846   
pneumonia      0.93538      0.99512    1.0447  0.947633   1.35547   1.35593   
case            0.9921      1.03082   1.10603   1.02182   1.40123   1.40252   

               data     model       detect      dataset        learn  \
2020        1.36572   1.37009      1.34284      1.36678      1.34649   
covid       1.40266   1.40533       1.3742      1.40384      1.38262   
pandem      1.08629   1.09521      1.07082      1.09729      1.05439   
health      1.32313   1.32659      1.29934      1.32913      1.30164   
polici     0.987681  0.998399     0.960186     0.997587      0.96464   
psycholog  0.989576  0.993666     0.955046     0.992406     0.958069   
impact     0.991414  0.999246     0.959072     0.996151      0.95205   
countri     1.03388   1.06051      1.01322      1.05761      1.02136   
develop     1.12068   1.11272      1.07797      1.11354      1.07576   
nation      1.01515   1.02071     0.988459      1.01716     0.989117   
imag        1.40569   1.40384      1.38148      1.40572      1.38078   
use         1.40355   1.40608      1.37941      1.40489       1.3846   
data              0   1.40248      1.37493      1.40314      1.37738   
model       1.40248         0      1.37725      1.40469      1.37937   
detect      1.37493   1.37725  2.58096e-08      1.37947      1.35634   
dataset     1.40314   1.40469      1.37947  2.98023e-08      1.37978   
learn       1.37738   1.37937      1.35634      1.37978  2.10734e-08   
pneumonia   1.35335   1.35669       1.3213      1.35549      1.32988   
case        1.39979   1.40221      1.37359      1.40191      1.37818   

          pneumonia      case  
2020        1.32241   1.36624  
covid       1.35689   1.40235  
pandem      1.06773   1.09547  
health      1.26718    1.3214  
polici     0.925534  0.985204  
psycholog  0.922983  0.988768  
impact      0.93538    0.9921  
countri     0.99512   1.03082  
develop      1.0447   1.10603  
nation     0.947633   1.02182  
imag        1.35547   1.40123  
use         1.35593   1.40252  
data        1.35335   1.39979  
model       1.35669   1.40221  
detect       1.3213   1.37359  
dataset     1.35549   1.40191  
learn       1.32988   1.37818  
pneumonia         0   1.35097  
case        1.35097         0  
Pairwise-distance
               2020     covid    pandem       health       polici  \
2020              0   1.40842   1.40475      1.39349      1.19192   
covid       1.40842         0    1.4034      1.39314      1.18645   
pandem      1.40475    1.4034         0      1.38992      1.19124   
health      1.39349   1.39314   1.38992  2.58096e-08      1.17268   
polici      1.19192   1.18645   1.19124      1.17268  1.82501e-08   
psycholog   1.40268   1.39913    1.3974      1.38387      1.18285   
impact      1.39118   1.38492   1.38301      1.37381       1.1802   
countri     1.39477   1.39324   1.39042      1.37963      1.17909   
develop     1.33854   1.33543   1.33842      1.32065      1.10161   
nation      1.08424   1.07596   1.09128       1.0778      0.54018   
imag       0.997544  0.995691   0.99091     0.975351     0.651343   
use         1.37027   1.36815   1.36417      1.35124       1.1333   
data        1.36714   1.36384   1.36214      1.34878       1.1427   
model       1.33811    1.3362   1.33299      1.31859       1.1196   
detect     0.998654  0.997841  0.992416     0.976091     0.653425   
dataset    0.997911  0.994124  0.991011     0.974497      0.64985   
learn       1.00684   1.00405  0.997215     0.987265      0.64682   
pneumonia  0.997606  0.995377  0.990899     0.974354     0.651499   
case        1.37294   1.36693   1.36747      1.35521      1.14194   

             psycholog    impact   countri   develop       nation       imag  \
2020           1.40268   1.39118   1.39477   1.33854      1.08424   0.997544   
covid          1.39913   1.38492   1.39324   1.33543      1.07596   0.995691   
pandem          1.3974   1.38301   1.39042   1.33842      1.09128    0.99091   
health         1.38387   1.37381   1.37963   1.32065       1.0778   0.975351   
polici         1.18285    1.1802   1.17909   1.10161      0.54018   0.651343   
psycholog  2.98023e-08    1.3793   1.38459   1.33477      1.08156   0.985905   
impact          1.3793         0   1.37429   1.32755      1.07049   0.971331   
countri        1.38459   1.37429         0   1.31263      1.07942   0.975373   
develop        1.33477   1.32755   1.31263         0     0.905358   0.891687   
nation         1.08156   1.07049   1.07942  0.905358  7.45058e-09   0.435323   
imag          0.985905  0.971331  0.975373  0.891687     0.435323          0   
use            1.36368   1.35025   1.35629   1.29097        1.045   0.940915   
data           1.36227   1.35031   1.35378   1.30716      1.01324   0.936453   
model          1.32548   1.31874   1.32279      1.26     0.994455   0.892189   
detect        0.983811  0.970599  0.966762  0.901796     0.440316  0.0698509   
dataset       0.985898  0.969204  0.976041  0.890047     0.429926  0.0466436   
learn          1.00011  0.990023  0.987257  0.903536     0.432831   0.162065   
pneumonia     0.985137  0.969793  0.976123  0.891667     0.431109  0.0402169   
case            1.3649   1.35457   1.35379   1.29358      1.04611    0.94385   

                use         data        model     detect    dataset     learn  \
2020        1.37027      1.36714      1.33811   0.998654   0.997911   1.00684   
covid       1.36815      1.36384       1.3362   0.997841   0.994124   1.00405   
pandem      1.36417      1.36214      1.33299   0.992416   0.991011  0.997215   
health      1.35124      1.34878      1.31859   0.976091   0.974497  0.987265   
polici       1.1333       1.1427       1.1196   0.653425    0.64985   0.64682   
psycholog   1.36368      1.36227      1.32548   0.983811   0.985898   1.00011   
impact      1.35025      1.35031      1.31874   0.970599   0.969204  0.990023   
countri     1.35629      1.35378      1.32279   0.966762   0.976041  0.987257   
develop     1.29097      1.30716         1.26   0.901796   0.890047  0.903536   
nation        1.045      1.01324     0.994455   0.440316   0.429926  0.432831   
imag       0.940915     0.936453     0.892189  0.0698509  0.0466436  0.162065   
use               0      1.30905      1.27562   0.944919   0.935694   0.90979   
data        1.30905  2.58096e-08      1.29622   0.919664   0.928952  0.962355   
model       1.27562      1.29622  2.10734e-08   0.901443   0.894936  0.911068   
detect     0.944919     0.919664     0.901443          0  0.0681092  0.170806   
dataset    0.935694     0.928952     0.894936  0.0681092          0  0.159519   
learn       0.90979     0.962355     0.911068   0.170806   0.159519         0   
pneumonia  0.939319     0.936539     0.893902  0.0682208  0.0408703  0.153328   
case        1.34192      1.32836      1.29484   0.945642    0.94495  0.954944   

           pneumonia      case  
2020        0.997606   1.37294  
covid       0.995377   1.36693  
pandem      0.990899   1.36747  
health      0.974354   1.35521  
polici      0.651499   1.14194  
psycholog   0.985137    1.3649  
impact      0.969793   1.35457  
countri     0.976123   1.35379  
develop     0.891667   1.29358  
nation      0.431109   1.04611  
imag       0.0402169   0.94385  
use         0.939319   1.34192  
data        0.936539   1.32836  
model       0.893902   1.29484  
detect     0.0682208  0.945642  
dataset    0.0408703   0.94495  
learn       0.153328  0.954944  
pneumonia          0  0.938609  
case        0.938609         0  

(-0.2274986076411022, 0.83863, -1.0285543569883562)

mantel_test_n(5)

Pairwise-distance
              trial    vaccin      stage       late     expect        covid  \
trial   3.29272e-10  0.334496  0.0652737  0.0457902  0.0703069     0.994227   
vaccin     0.334496         0   0.334689   0.337998   0.340189      1.04304   
stage     0.0652737  0.334689          0  0.0777504  0.0996353     0.994919   
late      0.0457902  0.337998  0.0777504          0  0.0815712     0.995989   
expect    0.0703069  0.340189  0.0996353  0.0815712          0     0.997507   
covid      0.994227   1.04304   0.994919   0.995989   0.997507  1.49012e-08   
imag       0.993552   1.04936    0.99207   0.992168   0.997729      1.40464   
use        0.995028   1.05179   0.996482   0.996607    1.00076      1.40725   
data       0.990223   1.04285   0.990314   0.990646   0.990675      1.40266   
model      0.994301   1.04815   0.993383   0.993458   0.992835      1.40533   

            imag       use      data     model  
trial   0.993552  0.995028  0.990223  0.994301  
vaccin   1.04936   1.05179   1.04285   1.04815  
stage    0.99207  0.996482  0.990314  0.993383  
late    0.992168  0.996607  0.990646  0.993458  
expect  0.997729   1.00076  0.990675  0.992835  
covid    1.40464   1.40725   1.40266   1.40533  
imag           0   1.40638   1.40569   1.40384  
use      1.40638         0   1.40355   1.40608  
data     1.40569   1.40355         0   1.40248  
model    1.40384   1.40608   1.40248         0  
Pairwise-distance
           trial    vaccin        stage      late    expect     covid imag  \
trial          0  0.945434     0.262596  0.366843  0.399192   1.08631    0   
vaccin  0.945434         0       0.8639  0.879678  0.885463   1.24608    0   
stage   0.262596    0.8639  7.45058e-09  0.242172  0.285506   1.01181    0   
late    0.366843  0.879678     0.242172         0  0.332784   1.00813    0   
expect  0.399192  0.885463     0.285506  0.332784         0   1.00751    0   
covid    1.08631   1.24608      1.01181   1.00813   1.00751         0    0   
imag           0         0            0         0         0         0    0   
use     0.746335   1.00057     0.642882  0.645935  0.624019   1.07927    0   
data    0.603135  0.912835     0.450508  0.526539  0.492882   1.05223    0   
model   0.551106  0.824359     0.403968  0.407541  0.367668  0.964775    0   

             use      data        model  
trial   0.746335  0.603135     0.551106  
vaccin   1.00057  0.912835     0.824359  
stage   0.642882  0.450508     0.403968  
late    0.645935  0.526539     0.407541  
expect  0.624019  0.492882     0.367668  
covid    1.07927   1.05223     0.964775  
imag           0         0            0  
use            0  0.591809     0.548085  
data    0.591809         0     0.445673  
model   0.548085  0.445673  2.63418e-09  

(0.01692738224462807, 0.49391, 0.057919303838280166)

mantel_test_n(10)

Pairwise-distance
                 trial    vaccin      stage       late     expect dose  \
trial      3.29272e-10  0.334496  0.0652737  0.0457902  0.0703069    0   
vaccin        0.334496         0   0.334689   0.337998   0.340189    0   
stage        0.0652737  0.334689          0  0.0777504  0.0996353    0   
late         0.0457902  0.337998  0.0777504          0  0.0815712    0   
expect       0.0703069  0.340189  0.0996353  0.0815712          0    0   
dose                 0         0          0          0          0    0   
underway             0         0          0          0          0    0   
novemb       0.0455113  0.329619  0.0726449  0.0536986  0.0863373    0   
addit         0.148744   0.34895   0.159562   0.144459    0.15068    0   
develop       0.513383   0.43076   0.528358   0.506117   0.512406    0   
covid         0.994227   1.04304   0.994919   0.995989   0.997507    0   
imag          0.993552   1.04936    0.99207   0.992168   0.997729    0   
use           0.995028   1.05179   0.996482   0.996607    1.00076    0   
data          0.990223   1.04285   0.990314   0.990646   0.990675    0   
model         0.994301   1.04815   0.993383   0.993458   0.992835    0   
detect        0.955299   1.00736   0.961046    0.95723   0.959009    0   
dataset        0.99098   1.04995   0.997247   0.992893     0.9881    0   
learn         0.958264   1.02483   0.953309   0.958989   0.962703    0   
pneumonia     0.923127  0.984885   0.929755   0.918661   0.925974    0   
case          0.988888   1.04339   0.989346   0.990395   0.993517    0   

          underway     novemb        addit   develop        covid      imag  \
trial            0  0.0455113     0.148744  0.513383     0.994227  0.993552   
vaccin           0   0.329619      0.34895   0.43076      1.04304   1.04936   
stage            0  0.0726449     0.159562  0.528358     0.994919   0.99207   
late             0  0.0536986     0.144459  0.506117     0.995989  0.992168   
expect           0  0.0863373      0.15068  0.512406     0.997507  0.997729   
dose             0          0            0         0            0         0   
underway         0          0            0         0            0         0   
novemb           0          0     0.156552  0.511979     0.995846  0.994225   
addit            0   0.156552  2.63418e-09  0.494355      1.00624   1.00479   
develop          0   0.511979     0.494355         0      1.12368   1.11847   
covid            0   0.995846      1.00624   1.12368  1.49012e-08   1.40464   
imag             0   0.994225      1.00479   1.11847      1.40464         0   
use              0   0.996138      1.00759   1.11817      1.40725   1.40638   
data             0   0.990878     0.995469   1.12068      1.40266   1.40569   
model            0   0.994174      1.00565   1.11272      1.40533   1.40384   
detect           0   0.957544      0.97319   1.07797       1.3742   1.38148   
dataset          0   0.993604      1.00563   1.11354      1.40384   1.40572   
learn            0   0.958197     0.975356   1.07576      1.38262   1.38078   
pneumonia        0   0.924444     0.936387    1.0447      1.35689   1.35547   
case             0   0.990169       1.0044   1.10603      1.40235   1.40123   

                use      data     model       detect      dataset  \
trial      0.995028  0.990223  0.994301     0.955299      0.99098   
vaccin      1.05179   1.04285   1.04815      1.00736      1.04995   
stage      0.996482  0.990314  0.993383     0.961046     0.997247   
late       0.996607  0.990646  0.993458      0.95723     0.992893   
expect      1.00076  0.990675  0.992835     0.959009       0.9881   
dose              0         0         0            0            0   
underway          0         0         0            0            0   
novemb     0.996138  0.990878  0.994174     0.957544     0.993604   
addit       1.00759  0.995469   1.00565      0.97319      1.00563   
develop     1.11817   1.12068   1.11272      1.07797      1.11354   
covid       1.40725   1.40266   1.40533       1.3742      1.40384   
imag        1.40638   1.40569   1.40384      1.38148      1.40572   
use               0   1.40355   1.40608      1.37941      1.40489   
data        1.40355         0   1.40248      1.37493      1.40314   
model       1.40608   1.40248         0      1.37725      1.40469   
detect      1.37941   1.37493   1.37725  2.58096e-08      1.37947   
dataset     1.40489   1.40314   1.40469      1.37947  2.98023e-08   
learn        1.3846   1.37738   1.37937      1.35634      1.37978   
pneumonia   1.35593   1.35335   1.35669       1.3213      1.35549   
case        1.40252   1.39979   1.40221      1.37359      1.40191   

                 learn pneumonia      case  
trial         0.958264  0.923127  0.988888  
vaccin         1.02483  0.984885   1.04339  
stage         0.953309  0.929755  0.989346  
late          0.958989  0.918661  0.990395  
expect        0.962703  0.925974  0.993517  
dose                 0         0         0  
underway             0         0         0  
novemb        0.958197  0.924444  0.990169  
addit         0.975356  0.936387    1.0044  
develop        1.07576    1.0447   1.10603  
covid          1.38262   1.35689   1.40235  
imag           1.38078   1.35547   1.40123  
use             1.3846   1.35593   1.40252  
data           1.37738   1.35335   1.39979  
model          1.37937   1.35669   1.40221  
detect         1.35634    1.3213   1.37359  
dataset        1.37978   1.35549   1.40191  
learn      2.10734e-08   1.32988   1.37818  
pneumonia      1.32988         0   1.35097  
case           1.37818   1.35097         0  
Pairwise-distance
              trial    vaccin        stage      late    expect         dose  \
trial             0  0.945434     0.262596  0.366843  0.399192     0.748852   
vaccin     0.945434         0       0.8639  0.879678  0.885463     0.992053   
stage      0.262596    0.8639  7.45058e-09  0.242172  0.285506      0.66449   
late       0.366843  0.879678     0.242172         0  0.332784     0.680024   
expect     0.399192  0.885463     0.285506  0.332784         0     0.573659   
dose       0.748852  0.992053      0.66449  0.680024  0.573659  1.05367e-08   
underway   0.403056  0.859493     0.244095  0.297631  0.289229     0.632046   
novemb     0.397539  0.849486     0.248758  0.287544  0.274898     0.639533   
addit      0.445364  0.860979     0.310649  0.350592  0.337981      0.63689   
develop    0.681659  0.980093     0.633853  0.652365  0.634082     0.847946   
covid       1.08631   1.24608      1.01181   1.00813   1.00751      1.11255   
imag              0         0            0         0         0            0   
use        0.746335   1.00057     0.642882  0.645935  0.624019     0.806973   
data       0.603135  0.912835     0.450508  0.526539  0.492882     0.734571   
model      0.551106  0.824359     0.403968  0.407541  0.367668      0.63727   
detect     0.559059  0.856794     0.398969  0.409769  0.366499     0.628541   
dataset           0         0            0         0         0            0   
learn      0.525798  0.835347     0.374226  0.376633  0.331358     0.616006   
pneumonia  0.558435  0.863203     0.410861  0.424944  0.380598     0.640221   
case        1.02514   1.20826     0.954097  0.951234  0.922763      1.09231   

              underway    novemb        addit      develop     covid imag  \
trial         0.403056  0.397539     0.445364     0.681659   1.08631    0   
vaccin        0.859493  0.849486     0.860979     0.980093   1.24608    0   
stage         0.244095  0.248758     0.310649     0.633853   1.01181    0   
late          0.297631  0.287544     0.350592     0.652365   1.00813    0   
expect        0.289229  0.274898     0.337981     0.634082   1.00751    0   
dose          0.632046  0.639533      0.63689     0.847946   1.11255    0   
underway   7.45058e-09  0.233259      0.29835     0.607784  0.974593    0   
novemb        0.233259         0     0.275329     0.598257  0.973511    0   
addit          0.29835  0.275329  5.26836e-09     0.629283  0.973493    0   
develop       0.607784  0.598257     0.629283  1.49012e-08   1.07383    0   
covid         0.974593  0.973511     0.973493      1.07383         0    0   
imag                 0         0            0            0         0    0   
use           0.622987  0.588229     0.611331     0.798822   1.07927    0   
data          0.454566  0.454692     0.495457     0.675609   1.05223    0   
model         0.323022  0.288787      0.32271     0.607936  0.964775    0   
detect         0.21524  0.291492     0.332087     0.595602  0.960245    0   
dataset              0         0            0            0         0    0   
learn         0.285118  0.250348     0.293733     0.574115  0.948083    0   
pneumonia     0.328271  0.299461     0.349617     0.610909  0.957274    0   
case          0.930944   0.90975     0.935559      1.02839    1.2678    0   

                use      data        model    detect dataset     learn  \
trial      0.746335  0.603135     0.551106  0.559059       0  0.525798   
vaccin      1.00057  0.912835     0.824359  0.856794       0  0.835347   
stage      0.642882  0.450508     0.403968  0.398969       0  0.374226   
late       0.645935  0.526539     0.407541  0.409769       0  0.376633   
expect     0.624019  0.492882     0.367668  0.366499       0  0.331358   
dose       0.806973  0.734571      0.63727  0.628541       0  0.616006   
underway   0.622987  0.454566     0.323022   0.21524       0  0.285118   
novemb     0.588229  0.454692     0.288787  0.291492       0  0.250348   
addit      0.611331  0.495457      0.32271  0.332087       0  0.293733   
develop    0.798822  0.675609     0.607936  0.595602       0  0.574115   
covid       1.07927   1.05223     0.964775  0.960245       0  0.948083   
imag              0         0            0         0       0         0   
use               0  0.591809     0.548085  0.582112       0  0.537211   
data       0.591809         0     0.445673  0.441224       0  0.425729   
model      0.548085  0.445673  2.63418e-09  0.236378       0  0.180477   
detect     0.582112  0.441224     0.236378         0       0  0.183061   
dataset           0         0            0         0       0         0   
learn      0.537211  0.425729     0.180477  0.183061       0         0   
pneumonia  0.578256  0.434679     0.241369  0.248207       0  0.199443   
case        1.04105  0.983477      0.88739  0.901723       0  0.880812   

          pneumonia         case  
trial      0.558435      1.02514  
vaccin     0.863203      1.20826  
stage      0.410861     0.954097  
late       0.424944     0.951234  
expect     0.380598     0.922763  
dose       0.640221      1.09231  
underway   0.328271     0.930944  
novemb     0.299461      0.90975  
addit      0.349617     0.935559  
develop    0.610909      1.02839  
covid      0.957274       1.2678  
imag              0            0  
use        0.578256      1.04105  
data       0.434679     0.983477  
model      0.241369      0.88739  
detect     0.248207     0.901723  
dataset           0            0  
learn      0.199443     0.880812  
pneumonia         0     0.879138  
case       0.879138  2.98023e-08  

(-0.07008842952957238, 0.62502, -0.34371128200507034)

Vertical mantel test

Considering two spaces in matel tests as

Article level -----> 2-3 Paragraph level ,

Article---->Paragraph level,

Paragraph----> 2-3 Paragraph level

def mantel_test_verticle(model_a,model_b,n):
    #all words from the cleaned documents
    words_science = dict(model_a.show_topic(0, topn=word_count_science))
    words_psy = dict(model_b.show_topic(0, topn=word_count_psy))
    #top 5 words from the documents
    top_words_science = dict(model_a.show_topic(0, topn=n)) 
    top_words_psy = dict(model_b.show_topic(0,topn=n)) 
    
    #Finding out the weights of top 5 law words in  model_a level and updating their weights w.r.t model b dataset 
    c={} 
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)
    #Finding out the weights of top 5 law words in model b and updating their weights w.r.t model a dataset
    b={} 
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for top 10 words in model a
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_a.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()

    #pairwise distance for top 10 words in model b
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_b.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')

mantel_test_verticle(model_science_articles,model_science_p,5)

Pairwise-distance
           support      2020    granada    covid   programm     imag  \
support          0  0.816641   0.188647  1.34497   0.187055  1.80116   
2020      0.816641         0   0.837019  1.50711   0.825008  1.99382   
granada   0.188647  0.837019          0  1.38721  0.0724919  1.77803   
covid      1.34497   1.50711    1.38721        0     1.3995  2.02757   
programm  0.187055  0.825008  0.0724919   1.3995          0  1.77059   
imag       1.80116   1.99382    1.77803  2.02757    1.77059        0   
data       1.54057   1.91755    1.62324  1.71834    1.61751  2.52912   
use       0.993312   1.24293    1.08391  1.47069    1.06767   1.9883   
model      1.58459   1.58989    1.53724  2.01725    1.53708  2.45821   

                 data        use    model  
support       1.54057   0.993312  1.58459  
2020          1.91755    1.24293  1.58989  
granada       1.62324    1.08391  1.53724  
covid         1.71834    1.47069  2.01725  
programm      1.61751    1.06767  1.53708  
imag          2.52912     1.9883  2.45821  
data      4.71216e-08    1.95244  2.20772  
use           1.95244  1.666e-08  1.81286  
model         2.20772    1.81286        0  
Pairwise-distance
           support     2020   granada    covid     programm     imag  \
support          0  2.31274   1.14543  4.29945     0.893249  3.43628   
2020       2.31274        0   1.24107   1.9984       1.7428  1.33987   
granada    1.14543  1.24107         0  3.23626     0.524939  2.29085   
covid      4.29945   1.9984   3.23626        0      3.72322  1.52372   
programm  0.893249   1.7428  0.524939  3.72322  3.72529e-08  2.66513   
imag       3.43628  1.33987   2.29085  1.52372      2.66513        0   
data        1.7865  1.52372  0.913734   3.2917     0.893249   1.9984   
use        2.85871  1.47712    1.7865  2.54797      1.98601  1.04988   
model      2.85871  1.47712    1.7865  2.54797      1.98601  1.04988   

                 data      use    model  
support        1.7865  2.85871  2.85871  
2020          1.52372  1.47712  1.47712  
granada      0.913734   1.7865   1.7865  
covid          3.2917  2.54797  2.54797  
programm     0.893249  1.98601  1.98601  
imag           1.9984  1.04988  1.04988  
data      1.86265e-08  1.14543  1.14543  
use           1.14543        0        0  
model         1.14543        0        0  

(0.07343425434503273, 0.3845, 0.3350390539943082)

mantel_test_verticle(model_science_articles,model_science_paragraph,5)

Pairwise-distance
         covid     imag        use         data    model
covid        0  2.02757    1.47069      1.71834  2.01725
imag   2.02757        0     1.9883      2.52912  2.45821
use    1.47069   1.9883  1.666e-08      1.95244  1.81286
data   1.71834  2.52912    1.95244  4.71216e-08  2.20772
model  2.01725  2.45821    1.81286      2.20772        0
Pairwise-distance
         covid     imag      use     data        model
covid        0  1.40421  1.40727  1.40273      1.40531
imag   1.40421        0  1.40628  1.40572       1.4035
use    1.40727  1.40628        0  1.40388      1.40595
data   1.40273  1.40572  1.40388        0      1.40268
model  1.40531   1.4035  1.40595  1.40268  2.10734e-08

(-0.32039903892431343, 0.7666666666666667, -0.8612915497586047)

mantel_test_verticle(model_science_p,model_science_paragraph,5)

Pairwise-distance
            covid     imag      use         data    model   support     2020  \
covid           0  1.52372  2.54797       3.2917  2.54797   4.29945   1.9984   
imag      1.52372        0  1.04988       1.9984  1.04988   3.43628  1.33987   
use       2.54797  1.04988        0      1.14543        0   2.85871  1.47712   
data       3.2917   1.9984  1.14543  1.86265e-08  1.14543    1.7865  1.52372   
model     2.54797  1.04988        0      1.14543        0   2.85871  1.47712   
support   4.29945  3.43628  2.85871       1.7865  2.85871         0  2.31274   
2020       1.9984  1.33987  1.47712      1.52372  1.47712   2.31274        0   
granada   3.23626  2.29085   1.7865     0.913734   1.7865   1.14543  1.24107   
programm  3.72322  2.66513  1.98601     0.893249  1.98601  0.893249   1.7428   

           granada     programm  
covid      3.23626      3.72322  
imag       2.29085      2.66513  
use         1.7865      1.98601  
data      0.913734     0.893249  
model       1.7865      1.98601  
support    1.14543     0.893249  
2020       1.24107       1.7428  
granada          0     0.524939  
programm  0.524939  3.72529e-08  
Pairwise-distance
             covid      imag       use      data        model      support  \
covid            0   1.40421   1.40727   1.40273      1.40531      1.06278   
imag       1.40421         0   1.40628   1.40572       1.4035      1.06418   
use        1.40727   1.40628         0   1.40388      1.40595      1.06854   
data       1.40273   1.40572   1.40388         0      1.40268      1.05647   
model      1.40531    1.4035   1.40595   1.40268  2.10734e-08      1.06304   
support    1.06278   1.06418   1.06854   1.05647      1.06304  7.45058e-09   
2020       1.37247   1.36764    1.3705   1.36589      1.37095      1.00675   
granada   0.994919  0.993477  0.995627   0.98926     0.993864     0.375134   
programm  0.995038  0.993206  0.995886  0.990081     0.993839     0.356717   

              2020      granada     programm  
covid      1.37247     0.994919     0.995038  
imag       1.36764     0.993477     0.993206  
use         1.3705     0.995627     0.995886  
data       1.36589      0.98926     0.990081  
model      1.37095     0.993864     0.993839  
support    1.00675     0.375134     0.356717  
2020             0     0.942932     0.943122  
granada   0.942932  2.32831e-10    0.0246327  
programm  0.943122    0.0246327  4.65661e-10  

(0.10610382106187127, 0.3988, 0.4236923509288503)

mantel_test_verticle(model_psy_articles,model_psy_p,5)

Pairwise-distance
           misinfodem    covid       articl       commun      mechan     2020  \
misinfodem   8.33e-09  1.34189     0.738596      0.47164    0.410331  1.55979   
covid         1.34189        0      1.23555       1.3068     1.27012  1.64398   
articl       0.738596  1.23555  1.17804e-08     0.689709    0.459477  1.31627   
commun        0.47164   1.3068     0.689709  1.17804e-08    0.371932   1.4797   
mechan       0.410331  1.27012     0.459477     0.371932  2.9451e-09   1.4595   
2020          1.55979  1.64398      1.31627       1.4797      1.4595        0   
psycholog     1.33778   1.5689        1.104      1.07665     1.13502   1.6757   
social        1.03298  1.63894      1.17862      1.03988     1.00005   1.6997   
epidem        1.16331  1.65599      1.11923      1.11811    0.981829  1.68517   

            psycholog   social     epidem  
misinfodem    1.33778  1.03298    1.16331  
covid          1.5689  1.63894    1.65599  
articl          1.104  1.17862    1.11923  
commun        1.07665  1.03988    1.11811  
mechan        1.13502  1.00005   0.981829  
2020           1.6757   1.6997    1.68517  
psycholog   1.666e-08  1.40467    1.36759  
social        1.40467        0    1.05316  
epidem        1.36759  1.05316  3.332e-08  
Pairwise-distance
           misinfodem    covid    articl    commun    mechan      2020  \
misinfodem          0  1.33926   2.07845   2.96288   2.96288   3.31627   
covid         1.33926        0   1.10881   1.65813   1.65813   2.07845   
articl        2.07845  1.10881         0   1.20521   1.20521   1.33926   
commun        2.96288  1.65813   1.20521         0         0  0.554403   
mechan        2.96288  1.65813   1.20521         0         0  0.554403   
2020          3.31627  2.07845   1.33926  0.554403  0.554403         0   
psycholog     2.55053  1.66321  0.554403   1.31385   1.31385   1.20521   
social        4.01777  2.67852   2.41043   1.20521   1.20521   1.31385   
epidem              0        0         0         0         0         0   

           psycholog   social epidem  
misinfodem   2.55053  4.01777      0  
covid        1.66321  2.67852      0  
articl      0.554403  2.41043      0  
commun       1.31385  1.20521      0  
mechan       1.31385  1.20521      0  
2020         1.20521  1.31385      0  
psycholog          0  2.45969      0  
social       2.45969        0      0  
epidem             0        0      0  

(-0.06374745393273847, 0.5867, -0.2263477316643351)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,5)

Pairwise-distance
              2020    covid    pandem   health    polici  psycholog   social  \
2020             0  1.64398   1.21093  1.63587   1.38208     1.6757   1.6997   
covid      1.64398        0   1.24569  1.92675   1.26996     1.5689  1.63894   
pandem     1.21093  1.24569         0  1.41086  0.788078    1.17936  1.32541   
health     1.63587  1.92675   1.41086        0   1.46491    1.41224  1.67666   
polici     1.38208  1.26996  0.788078  1.46491  8.33e-09     1.0708  1.13705   
psycholog   1.6757   1.5689   1.17936  1.41224    1.0708  1.666e-08  1.40467   
social      1.6997  1.63894   1.32541  1.67666   1.13705    1.40467        0   
epidem     1.68517  1.65599   1.34219  1.68828   1.03278    1.36759  1.05316   

              epidem  
2020         1.68517  
covid        1.65599  
pandem       1.34219  
health       1.68828  
polici       1.03278  
psycholog    1.36759  
social       1.05316  
epidem     3.332e-08  
Pairwise-distance
                  2020    covid       pandem   health       polici psycholog  \
2020       2.98023e-08  1.40859      1.40457  1.39359       1.1861   1.40269   
covid          1.40859        0      1.40314  1.39289      1.18071   1.39959   
pandem         1.40457  1.40314  2.98023e-08  1.38902      1.18354   1.39676   
health         1.39359  1.39289      1.38902        0      1.16427   1.38385   
polici          1.1861  1.18071      1.18354  1.16427  1.05367e-08   1.17642   
psycholog      1.40269  1.39959      1.39676  1.38385      1.17642         0   
social         1.38116  1.37955      1.37681  1.36128       1.1579   1.37572   
epidem         1.38005  1.37922       1.3742  1.36229      1.14678   1.36667   

            social   epidem  
2020       1.38116  1.38005  
covid      1.37955  1.37922  
pandem     1.37681   1.3742  
health     1.36128  1.36229  
polici      1.1579  1.14678  
psycholog  1.37572  1.36667  
social           0  1.34363  
epidem     1.34363        0  

(0.5650443081417844, 0.0453, 1.6640248141204788)

mantel_test_verticle(model_psy_p,model_psy_paragraph,5)

Pairwise-distance
                2020    covid   pandem       health polici misinfodem  \
2020               0  2.07845  1.31385     0.554403      0    3.31627   
covid        2.07845        0  2.67852      1.65813      0    1.33926   
pandem       1.31385  2.67852        0      1.20521      0    4.01777   
health      0.554403  1.65813  1.20521            0      0    2.96288   
polici             0        0        0            0      0          0   
misinfodem   3.31627  1.33926  4.01777      2.96288      0          0   
articl       1.33926  1.10881  2.41043      1.20521      0    2.07845   
commun      0.554403  1.65813  1.20521            0      0    2.96288   
mechan      0.554403  1.65813  1.20521  1.31709e-08      0    2.96288   

             articl    commun       mechan  
2020        1.33926  0.554403     0.554403  
covid       1.10881   1.65813      1.65813  
pandem      2.41043   1.20521      1.20521  
health      1.20521         0  1.31709e-08  
polici            0         0            0  
misinfodem  2.07845   2.96288      2.96288  
articl            0   1.20521      1.20521  
commun      1.20521         0            0  
mechan      1.20521         0            0  
Pairwise-distance
                   2020     covid       pandem    health       polici  \
2020        2.98023e-08   1.40859      1.40457   1.39359       1.1861   
covid           1.40859         0      1.40314   1.39289      1.18071   
pandem          1.40457   1.40314  2.98023e-08   1.38902      1.18354   
health          1.39359   1.39289      1.38902         0      1.16427   
polici           1.1861   1.18071      1.18354   1.16427  1.05367e-08   
misinfodem      1.00964  0.998188      0.99604  0.990675     0.666441   
articl          1.25618   1.24364      1.23712   1.23471      0.99842   
commun           1.3593   1.35413      1.35723   1.33539       1.1385   
mechan         0.997926  0.993957     0.991672  0.975204     0.640464   

           misinfodem    articl       commun    mechan  
2020          1.00964   1.25618       1.3593  0.997926  
covid        0.998188   1.24364      1.35413  0.993957  
pandem        0.99604   1.23712      1.35723  0.991672  
health       0.990675   1.23471      1.33539  0.975204  
polici       0.666441   0.99842       1.1385  0.640464  
misinfodem          0  0.762354     0.952795  0.124054  
articl       0.762354         0      1.19818  0.754808  
commun       0.952795   1.19818  2.10734e-08  0.927779  
mechan       0.124054  0.754808     0.927779         0  

(-0.11802488631885512, 0.6568, -0.4046262850059532)

mantel_test_verticle(model_science_articles,model_science_p,10)

Pairwise-distance
           support      2020    granada    covid  research   programm  \
support          0  0.816641   0.188647  1.34497  0.327252   0.187055   
2020      0.816641         0   0.837019  1.50711  0.857407   0.825008   
granada   0.188647  0.837019          0  1.38721  0.268729  0.0724919   
covid      1.34497   1.50711    1.38721        0   1.32888     1.3995   
research  0.327252  0.857407   0.268729  1.32888         0   0.247326   
programm  0.187055  0.825008  0.0724919   1.3995  0.247326          0   
tabik     0.207205  0.849983  0.0543689  1.38042  0.295629   0.126861   
garcía    0.184286  0.830191  0.0420849  1.39198  0.254454  0.0354088   
decsai    0.184807  0.827432  0.0543689  1.39609  0.250891   0.018123   
project   0.280371  0.843909    0.24052  1.42673  0.293543   0.201352   
imag       1.80116   1.99382    1.77803  2.02757    1.7596    1.77059   
data       1.54057   1.91755    1.62324  1.71834   1.56879    1.61751   
use       0.993312   1.24293    1.08391  1.47069   1.09865    1.06767   
model      1.58459   1.58989    1.53724  2.01725   1.56162    1.53708   
dataset    1.27474   1.59928    1.26197  1.99076   1.26775    1.27385   
class      1.05441   1.27301    1.02538  1.43477    1.1235     1.0666   
learn      1.24747   1.45402    1.22658  1.67678   1.24625    1.21551   
perform   0.790359    1.0249   0.807618  1.47021  0.819504   0.779769   
train     0.804701   1.02548   0.756157  1.32819  0.727077   0.735173   

              tabik     garcía     decsai      project     imag         data  \
support    0.207205   0.184286   0.184807     0.280371  1.80116      1.54057   
2020       0.849983   0.830191   0.827432     0.843909  1.99382      1.91755   
granada   0.0543689  0.0420849  0.0543689      0.24052  1.77803      1.62324   
covid       1.38042    1.39198    1.39609      1.42673  2.02757      1.71834   
research   0.295629   0.254454   0.250891     0.293543   1.7596      1.56879   
programm   0.126861  0.0354088   0.018123     0.201352  1.77059      1.61751   
tabik             0  0.0951653   0.108738     0.278797  1.78553      1.62964   
garcía    0.0951653          0  0.0199448     0.206467  1.77333      1.62034   
decsai     0.108738  0.0199448          0     0.209485  1.77218      1.61864   
project    0.278797   0.206467   0.209485  5.10106e-09  1.74898      1.64049   
imag        1.78553    1.77333    1.77218      1.74898        0      2.52912   
data        1.62964    1.62034    1.61864      1.64049  2.52912  4.71216e-08   
use         1.09907    1.07214     1.0713       1.0301   1.9883      1.95244   
model        1.5396    1.53774     1.5368      1.57066  2.45821      2.20772   
dataset     1.25573    1.26927     1.2705      1.26058  1.97696      1.90756   
class      0.996813    1.03919    1.05598      1.02701  2.04976      2.03687   
learn       1.23761    1.22072    1.21788      1.23169  2.19157      2.13977   
perform    0.832046   0.793742   0.786197     0.806445  1.88734      1.76081   
train      0.775977   0.746538   0.739809     0.768763  1.58582      1.79814   

                use    model  dataset     class    learn   perform     train  
support    0.993312  1.58459  1.27474   1.05441  1.24747  0.790359  0.804701  
2020        1.24293  1.58989  1.59928   1.27301  1.45402    1.0249   1.02548  
granada     1.08391  1.53724  1.26197   1.02538  1.22658  0.807618  0.756157  
covid       1.47069  2.01725  1.99076   1.43477  1.67678   1.47021   1.32819  
research    1.09865  1.56162  1.26775    1.1235  1.24625  0.819504  0.727077  
programm    1.06767  1.53708  1.27385    1.0666  1.21551  0.779769  0.735173  
tabik       1.09907   1.5396  1.25573  0.996813  1.23761  0.832046  0.775977  
garcía      1.07214  1.53774  1.26927   1.03919  1.22072  0.793742  0.746538  
decsai       1.0713   1.5368   1.2705   1.05598  1.21788  0.786197  0.739809  
project      1.0301  1.57066  1.26058   1.02701  1.23169  0.806445  0.768763  
imag         1.9883  2.45821  1.97696   2.04976  2.19157   1.88734   1.58582  
data        1.95244  2.20772  1.90756   2.03687  2.13977   1.76081   1.79814  
use       1.666e-08  1.81286  1.55914   1.47649  1.47647   1.05944   1.37724  
model       1.81286        0  2.00352   1.82546    1.924   1.26853   1.56263  
dataset     1.55914  2.00352        0   1.59981  1.83856   1.50625   1.52998  
class       1.47649  1.82546  1.59981         0  1.56226   1.41108   1.26185  
learn       1.47647    1.924  1.83856   1.56226        0   1.34012   1.29341  
perform     1.05944  1.26853  1.50625   1.41108  1.34012         0  0.904781  
train       1.37724  1.56263  1.52998   1.26185  1.29341  0.904781         0  
Pairwise-distance
           support      2020   granada    covid     research     programm  \
support          0   2.31274   1.14543  4.29945     0.893249     0.893249   
2020       2.31274         0   1.24107   1.9984       1.7428       1.7428   
granada    1.14543   1.24107         0  3.23626     0.524939     0.524939   
covid      4.29945    1.9984   3.23626        0      3.72322      3.72322   
research  0.893249    1.7428  0.524939  3.72322  3.72529e-08  3.72529e-08   
programm  0.893249    1.7428  0.524939  3.72322  3.72529e-08  3.72529e-08   
tabik      1.55841   1.04988  0.446625  2.97988     0.770914     0.770914   
garcía     2.29085  0.596458   1.14543  2.25512      1.54183      1.54183   
decsai     1.33987   1.57482  0.596458   3.4856     0.446625     0.446625   
project    1.33987   1.57482  0.596458   3.4856     0.446625     0.446625   
imag       3.43628   1.33987   2.29085  1.52372      2.66513      2.66513   
data        1.7865   1.52372  0.913734   3.2917     0.893249     0.893249   
use        2.85871   1.47712    1.7865  2.54797      1.98601      1.98601   
model      2.85871   1.47712    1.7865  2.54797      1.98601      1.98601   
dataset    2.85871   1.47712    1.7865  2.54797      1.98601      1.98601   
class            0         0         0        0            0            0   
learn      2.85871   1.47712    1.7865  2.54797      1.98601      1.98601   
perform          0         0         0        0            0            0   
train            0         0         0        0            0            0   

             tabik    garcía    decsai   project     imag         data  \
support    1.55841   2.29085   1.33987   1.33987  3.43628       1.7865   
2020       1.04988  0.596458   1.57482   1.57482  1.33987      1.52372   
granada   0.446625   1.14543  0.596458  0.596458  2.29085     0.913734   
covid      2.97988   2.25512    3.4856    3.4856  1.52372       3.2917   
research  0.770914   1.54183  0.446625  0.446625  2.66513     0.893249   
programm  0.770914   1.54183  0.446625  0.446625  2.66513     0.893249   
tabik            0  0.770914  0.524939  0.524939  1.90083     0.596458   
garcía    0.770914         0   1.24107   1.24107  1.14543      1.04988   
decsai    0.524939   1.24107         0         0  2.31274     0.446625   
project   0.524939   1.24107         0         0  2.31274     0.446625   
imag       1.90083   1.14543   2.31274   2.31274        0       1.9984   
data      0.596458   1.04988  0.446625  0.446625   1.9984  1.86265e-08   
use        1.33987  0.913734   1.55841   1.55841  1.04988      1.14543   
model      1.33987  0.913734   1.55841   1.55841  1.04988      1.14543   
dataset    1.33987  0.913734   1.55841   1.55841  1.04988      1.14543   
class            0         0         0         0        0            0   
learn      1.33987  0.913734   1.55841   1.55841  1.04988      1.14543   
perform          0         0         0         0        0            0   
train            0         0         0         0        0            0   

                  use        model      dataset class     learn perform train  
support       2.85871      2.85871      2.85871     0   2.85871       0     0  
2020          1.47712      1.47712      1.47712     0   1.47712       0     0  
granada        1.7865       1.7865       1.7865     0    1.7865       0     0  
covid         2.54797      2.54797      2.54797     0   2.54797       0     0  
research      1.98601      1.98601      1.98601     0   1.98601       0     0  
programm      1.98601      1.98601      1.98601     0   1.98601       0     0  
tabik         1.33987      1.33987      1.33987     0   1.33987       0     0  
garcía       0.913734     0.913734     0.913734     0  0.913734       0     0  
decsai        1.55841      1.55841      1.55841     0   1.55841       0     0  
project       1.55841      1.55841      1.55841     0   1.55841       0     0  
imag          1.04988      1.04988      1.04988     0   1.04988       0     0  
data          1.14543      1.14543      1.14543     0   1.14543       0     0  
use                 0            0  6.58545e-09     0         0       0     0  
model               0            0  6.58545e-09     0         0       0     0  
dataset   6.58545e-09  6.58545e-09            0     0         0       0     0  
class               0            0            0     0         0       0     0  
learn               0            0            0     0         0       0     0  
perform             0            0            0     0         0       0     0  
train               0            0            0     0         0       0     0  

(0.1922992491228527, 0.1557, 1.0440487307871915)

mantel_test_verticle(model_science_articles,model_science_paragraph,10)

Pairwise-distance
             covid     imag        use         data    model     detect  \
covid            0  2.02757    1.47069      1.71834  2.01725    1.49496   
imag       2.02757        0     1.9883      2.52912  2.45821    1.80145   
use        1.47069   1.9883  1.666e-08      1.95244  1.81286    1.35039   
data       1.71834  2.52912    1.95244  4.71216e-08  2.20772    1.72029   
model      2.01725  2.45821    1.81286      2.20772        0    1.53885   
detect     1.49496  1.80145    1.35039      1.72029  1.53885  1.666e-08   
dataset    1.99076  1.97696    1.55914      1.90756  2.00352    1.55985   
learn      1.67678  2.19157    1.47647      2.13977    1.924    1.36813   
pneumonia  1.51124  1.65107    1.29053      1.92202  1.62318    1.14483   
case        1.8398  2.10333     1.4916      1.79869  1.76951    1.42734   
class      1.43477  2.04976    1.47649      2.03687  1.82546    1.27213   
perform    1.47021  1.88734    1.05944      1.76081  1.26853    0.97356   
train      1.32819  1.58582    1.37724      1.79814  1.56263    0.91949   

           dataset    learn    pneumonia     case    class   perform     train  
covid      1.99076  1.67678      1.51124   1.8398  1.43477   1.47021   1.32819  
imag       1.97696  2.19157      1.65107  2.10333  2.04976   1.88734   1.58582  
use        1.55914  1.47647      1.29053   1.4916  1.47649   1.05944   1.37724  
data       1.90756  2.13977      1.92202  1.79869  2.03687   1.76081   1.79814  
model      2.00352    1.924      1.62318  1.76951  1.82546   1.26853   1.56263  
detect     1.55985  1.36813      1.14483  1.42734  1.27213   0.97356   0.91949  
dataset          0  1.83856      1.62487  1.56521  1.59981   1.50625   1.52998  
learn      1.83856        0      1.44422  1.70748  1.56226   1.34012   1.29341  
pneumonia  1.62487  1.44422  2.35608e-08  1.45796  1.27669  0.859783  0.858428  
case       1.56521  1.70748      1.45796        0  1.60019   1.44212   1.35873  
class      1.59981  1.56226      1.27669  1.60019        0   1.41108   1.26185  
perform    1.50625  1.34012     0.859783  1.44212  1.41108         0  0.904781  
train      1.52998  1.29341     0.858428  1.35873  1.26185  0.904781         0  
Pairwise-distance
             covid     imag      use     data        model   detect  \
covid            0  1.40421  1.40727  1.40273      1.40531  1.37374   
imag       1.40421        0  1.40628  1.40572       1.4035  1.38102   
use        1.40727  1.40628        0  1.40388      1.40595  1.37802   
data       1.40273  1.40572  1.40388        0      1.40268  1.37488   
model      1.40531   1.4035  1.40595  1.40268  2.10734e-08  1.37696   
detect     1.37374  1.38102  1.37802  1.37488      1.37696        0   
dataset    1.40378  1.40559  1.40461  1.40323      1.40448  1.37935   
learn      1.38162  1.37922  1.38403  1.37655      1.37925  1.35772   
pneumonia  1.35568  1.35395  1.35473  1.35165      1.35503  1.32098   
case       1.40363  1.40305  1.40457   1.4018      1.40358  1.37442   
class      1.40066  1.40139  1.40288  1.39873      1.40067  1.37328   
perform    1.39968  1.39832  1.39973  1.39625      1.39996  1.36945   
train      1.40488  1.40369  1.40465  1.40045      1.40176  1.37614   

               dataset    learn  pneumonia         case        class  \
covid          1.40378  1.38162    1.35568      1.40363      1.40066   
imag           1.40559  1.37922    1.35395      1.40305      1.40139   
use            1.40461  1.38403    1.35473      1.40457      1.40288   
data           1.40323  1.37655    1.35165       1.4018      1.39873   
model          1.40448  1.37925    1.35503      1.40358      1.40067   
detect         1.37935  1.35772    1.32098      1.37442      1.37328   
dataset    1.49012e-08  1.37892    1.35471      1.40317      1.40065   
learn          1.37892        0    1.33013      1.37961      1.37412   
pneumonia      1.35471  1.33013  3.332e-08      1.35182      1.34286   
case           1.40317  1.37961    1.35182  2.10734e-08      1.40016   
class          1.40065  1.37412    1.34286      1.40016  2.58096e-08   
perform        1.39746  1.37242    1.34808      1.39701       1.3957   
train          1.40271  1.37919    1.35335      1.40102      1.39916   

               perform        train  
covid          1.39968      1.40488  
imag           1.39832      1.40369  
use            1.39973      1.40465  
data           1.39625      1.40045  
model          1.39996      1.40176  
detect         1.36945      1.37614  
dataset        1.39746      1.40271  
learn          1.37242      1.37919  
pneumonia      1.34808      1.35335  
case           1.39701      1.40102  
class           1.3957      1.39916  
perform    3.65002e-08      1.39794  
train          1.39794  1.49012e-08  

(0.3987595628510933, 0.0671, 1.4780991767816107)

mantel_test_verticle(model_science_p,model_science_paragraph,10)

Pairwise-distance
             covid      imag          use         data        model  \
covid            0   1.52372      2.54797       3.2917      2.54797   
imag       1.52372         0      1.04988       1.9984      1.04988   
use        2.54797   1.04988            0      1.14543            0   
data        3.2917    1.9984      1.14543  1.86265e-08      1.14543   
model      2.54797   1.04988            0      1.14543            0   
detect     2.54797   1.04988            0      1.14543            0   
dataset    2.54797   1.04988  6.58545e-09      1.14543  6.58545e-09   
learn      2.54797   1.04988            0      1.14543            0   
pneumonia        0         0            0            0            0   
case       2.03258  0.524939     0.524939      1.54183     0.524939   
support    4.29945   3.43628      2.85871       1.7865      2.85871   
2020        1.9984   1.33987      1.47712      1.52372      1.47712   
granada    3.23626   2.29085       1.7865     0.913734       1.7865   
research   3.72322   2.66513      1.98601     0.893249      1.98601   
programm   3.72322   2.66513      1.98601     0.893249      1.98601   
tabik      2.97988   1.90083      1.33987     0.596458      1.33987   
garcía     2.25512   1.14543     0.913734      1.04988     0.913734   
decsai      3.4856   2.31274      1.55841     0.446625      1.55841   
project     3.4856   2.31274      1.55841     0.446625      1.55841   

                detect      dataset     learn pneumonia      case   support  \
covid          2.54797      2.54797   2.54797         0   2.03258   4.29945   
imag           1.04988      1.04988   1.04988         0  0.524939   3.43628   
use                  0  6.58545e-09         0         0  0.524939   2.85871   
data           1.14543      1.14543   1.14543         0   1.54183    1.7865   
model                0  6.58545e-09         0         0  0.524939   2.85871   
detect               0  6.58545e-09         0         0  0.524939   2.85871   
dataset    6.58545e-09            0         0         0  0.524939   2.85871   
learn                0            0         0         0  0.524939   2.85871   
pneumonia            0            0         0         0         0         0   
case          0.524939     0.524939  0.524939         0         0   3.11682   
support        2.85871      2.85871   2.85871         0   3.11682         0   
2020           1.47712      1.47712   1.47712         0   1.30882   2.31274   
granada         1.7865       1.7865    1.7865         0   1.98601   1.14543   
research       1.98601      1.98601   1.98601         0   2.29085  0.893249   
programm       1.98601      1.98601   1.98601         0   2.29085  0.893249   
tabik          1.33987      1.33987   1.33987         0   1.55841   1.55841   
garcía        0.913734     0.913734  0.913734         0  0.893249   2.29085   
decsai         1.55841      1.55841   1.55841         0   1.90083   1.33987   
project        1.55841      1.55841   1.55841         0   1.90083   1.33987   

               2020   granada     research     programm     tabik    garcía  \
covid        1.9984   3.23626      3.72322      3.72322   2.97988   2.25512   
imag        1.33987   2.29085      2.66513      2.66513   1.90083   1.14543   
use         1.47712    1.7865      1.98601      1.98601   1.33987  0.913734   
data        1.52372  0.913734     0.893249     0.893249  0.596458   1.04988   
model       1.47712    1.7865      1.98601      1.98601   1.33987  0.913734   
detect      1.47712    1.7865      1.98601      1.98601   1.33987  0.913734   
dataset     1.47712    1.7865      1.98601      1.98601   1.33987  0.913734   
learn       1.47712    1.7865      1.98601      1.98601   1.33987  0.913734   
pneumonia         0         0            0            0         0         0   
case        1.30882   1.98601      2.29085      2.29085   1.55841  0.893249   
support     2.31274   1.14543     0.893249     0.893249   1.55841   2.29085   
2020              0   1.24107       1.7428       1.7428   1.04988  0.596458   
granada     1.24107         0     0.524939     0.524939  0.446625   1.14543   
research     1.7428  0.524939  3.72529e-08  3.72529e-08  0.770914   1.54183   
programm     1.7428  0.524939  3.72529e-08  3.72529e-08  0.770914   1.54183   
tabik       1.04988  0.446625     0.770914     0.770914         0  0.770914   
garcía     0.596458   1.14543      1.54183      1.54183  0.770914         0   
decsai      1.57482  0.596458     0.446625     0.446625  0.524939   1.24107   
project     1.57482  0.596458     0.446625     0.446625  0.524939   1.24107   

             decsai   project  
covid        3.4856    3.4856  
imag        2.31274   2.31274  
use         1.55841   1.55841  
data       0.446625  0.446625  
model       1.55841   1.55841  
detect      1.55841   1.55841  
dataset     1.55841   1.55841  
learn       1.55841   1.55841  
pneumonia         0         0  
case        1.90083   1.90083  
support     1.33987   1.33987  
2020        1.57482   1.57482  
granada    0.596458  0.596458  
research   0.446625  0.446625  
programm   0.446625  0.446625  
tabik      0.524939  0.524939  
garcía      1.24107   1.24107  
decsai            0         0  
project           0         0  
Pairwise-distance
              covid      imag       use      data        model    detect  \
covid             0   1.40421   1.40727   1.40273      1.40531   1.37374   
imag        1.40421         0   1.40628   1.40572       1.4035   1.38102   
use         1.40727   1.40628         0   1.40388      1.40595   1.37802   
data        1.40273   1.40572   1.40388         0      1.40268   1.37488   
model       1.40531    1.4035   1.40595   1.40268  2.10734e-08   1.37696   
detect      1.37374   1.38102   1.37802   1.37488      1.37696         0   
dataset     1.40378   1.40559   1.40461   1.40323      1.40448   1.37935   
learn       1.38162   1.37922   1.38403   1.37655      1.37925   1.35772   
pneumonia   1.35568   1.35395   1.35473   1.35165      1.35503   1.32098   
case        1.40363   1.40305   1.40457    1.4018      1.40358   1.37442   
support     1.06278   1.06418   1.06854   1.05647      1.06304   1.03184   
2020        1.37247   1.36764    1.3705   1.36589      1.37095   1.34249   
granada    0.994919  0.993477  0.995627   0.98926     0.993864  0.954406   
research    1.10123   1.09918   1.10233   1.09244      1.10677   1.05293   
programm   0.995038  0.993206  0.995886  0.990081     0.993839  0.954995   
tabik      0.989382  0.990932  0.997691  0.990737     0.996166  0.960675   
garcía     0.994712  0.992939    0.9954  0.989892      0.99337  0.954772   
decsai     0.994692  0.992952  0.995401  0.989883     0.993313   0.95459   
project    0.998161  0.993764  0.994315  0.991023     0.994203  0.957608   

               dataset     learn  pneumonia         case      support  \
covid          1.40378   1.38162    1.35568      1.40363      1.06278   
imag           1.40559   1.37922    1.35395      1.40305      1.06418   
use            1.40461   1.38403    1.35473      1.40457      1.06854   
data           1.40323   1.37655    1.35165       1.4018      1.05647   
model          1.40448   1.37925    1.35503      1.40358      1.06304   
detect         1.37935   1.35772    1.32098      1.37442      1.03184   
dataset    1.49012e-08   1.37892    1.35471      1.40317      1.06129   
learn          1.37892         0    1.33013      1.37961      1.03572   
pneumonia      1.35471   1.33013  3.332e-08      1.35182     0.998452   
case           1.40317   1.37961    1.35182  2.10734e-08      1.06188   
support        1.06129   1.03572   0.998452      1.06188  7.45058e-09   
2020           1.36788   1.34561    1.32336      1.36907      1.00675   
granada       0.992726  0.957442   0.921466     0.991552     0.375134   
research        1.0982    1.0727    1.04357      1.10407     0.371799   
programm      0.992533  0.958047   0.921611     0.991307     0.356717   
tabik         0.984802  0.959439   0.924422     0.992796     0.379168   
garcía        0.992227  0.957386   0.921346     0.991095     0.378892   
decsai        0.992279  0.957428   0.921096     0.991277     0.378799   
project       0.993346  0.962198   0.921511     0.989514     0.354371   

               2020      granada  research     programm      tabik  \
covid       1.37247     0.994919   1.10123     0.995038   0.989382   
imag        1.36764     0.993477   1.09918     0.993206   0.990932   
use          1.3705     0.995627   1.10233     0.995886   0.997691   
data        1.36589      0.98926   1.09244     0.990081   0.990737   
model       1.37095     0.993864   1.10677     0.993839   0.996166   
detect      1.34249     0.954406   1.05293     0.954995   0.960675   
dataset     1.36788     0.992726    1.0982     0.992533   0.984802   
learn       1.34561     0.957442    1.0727     0.958047   0.959439   
pneumonia   1.32336     0.921466   1.04357     0.921611   0.924422   
case        1.36907     0.991552   1.10407     0.991307   0.992796   
support     1.00675     0.375134  0.371799     0.356717   0.379168   
2020              0     0.942932   1.05537     0.943122   0.945404   
granada    0.942932  2.32831e-10   0.46932    0.0246327  0.0414022   
research    1.05537      0.46932         0     0.453623   0.469499   
programm   0.943122    0.0246327  0.453623  4.65661e-10  0.0437944   
tabik      0.945404    0.0414022  0.469499    0.0437944          0   
garcía     0.942555    0.0164708  0.471534    0.0241717   0.038013   
decsai     0.942713    0.0144416  0.472001    0.0235131  0.0393642   
project    0.939722    0.0611338  0.456785    0.0586793  0.0782135   

               garcía       decsai    project  
covid        0.994712     0.994692   0.998161  
imag         0.992939     0.992952   0.993764  
use            0.9954     0.995401   0.994315  
data         0.989892     0.989883   0.991023  
model         0.99337     0.993313   0.994203  
detect       0.954772      0.95459   0.957608  
dataset      0.992227     0.992279   0.993346  
learn        0.957386     0.957428   0.962198  
pneumonia    0.921346     0.921096   0.921511  
case         0.991095     0.991277   0.989514  
support      0.378892     0.378799   0.354371  
2020         0.942555     0.942713   0.939722  
granada     0.0164708    0.0144416  0.0611338  
research     0.471534     0.472001   0.456785  
programm    0.0241717    0.0235131  0.0586793  
tabik        0.038013    0.0393642  0.0782135  
garcía              0   0.00732907  0.0653737  
decsai     0.00732907  5.04093e-11  0.0646922  
project     0.0653737    0.0646922          0  

(0.07489061944996013, 0.3574, 0.42684320312414537)

mantel_test_verticle(model_psy_articles,model_psy_p,10)

Pairwise-distance
           misinfodem    covid       articl       commun      mechan   health  \
misinfodem   8.33e-09  1.34189     0.738596      0.47164    0.410331  1.46436   
covid         1.34189        0      1.23555       1.3068     1.27012  1.92675   
articl       0.738596  1.23555  1.17804e-08     0.689709    0.459477  1.41907   
commun        0.47164   1.3068     0.689709  1.17804e-08    0.371932  1.39573   
mechan       0.410331  1.27012     0.459477     0.371932  2.9451e-09  1.39619   
health        1.46436  1.92675      1.41907      1.39573     1.39619        0   
strategi     0.367223  1.29332      0.52747     0.336412    0.177941  1.36005   
psycholog     1.33778   1.5689        1.104      1.07665     1.13502  1.41224   
misinform    0.219782  1.29879     0.573296     0.366512    0.193868  1.41432   
inform       0.400083  1.28014     0.618093     0.336857    0.346339  1.50084   
2020          1.55979  1.64398      1.31627       1.4797      1.4595  1.63587   
social        1.03298  1.63894      1.17862      1.03988     1.00005  1.67666   
epidem        1.16331  1.65599      1.11923      1.11811    0.981829  1.68828   
pandem       0.935609  1.24569     0.751603     0.941839    0.805615  1.41086   
time         0.835836  1.41466     0.708098     0.728133    0.630183    1.495   
group         1.82751  2.09422      1.67686      1.83891      1.7099  2.24603   
peopl         1.21721  1.65426      1.10551      1.12996     1.11525  1.88704   

            strategi  psycholog misinform    inform     2020    social  \
misinfodem  0.367223    1.33778  0.219782  0.400083  1.55979   1.03298   
covid        1.29332     1.5689   1.29879   1.28014  1.64398   1.63894   
articl       0.52747      1.104  0.573296  0.618093  1.31627   1.17862   
commun      0.336412    1.07665  0.366512  0.336857   1.4797   1.03988   
mechan      0.177941    1.13502  0.193868  0.346339   1.4595   1.00005   
health       1.36005    1.41224   1.41432   1.50084  1.63587   1.67666   
strategi           0    1.14429  0.195458  0.360005  1.44692   1.02535   
psycholog    1.14429  1.666e-08   1.21392   1.19198   1.6757   1.40467   
misinform   0.195458    1.21392         0  0.309496  1.48789  0.985289   
inform      0.360005    1.19198  0.309496  8.33e-09  1.49885  0.943419   
2020         1.44692     1.6757   1.48789   1.49885        0    1.6997   
social       1.02535    1.40467  0.985289  0.943419   1.6997         0   
epidem        1.0459    1.36759   1.03317   1.10397  1.68517   1.05316   
pandem      0.876623    1.17936  0.850458  0.918664  1.21093   1.32541   
time        0.619814    1.14433  0.695799  0.758514  1.37622  0.916963   
group        1.74272    2.04504   1.76019   1.75681  2.23965   1.78649   
peopl         1.1706    1.53597   1.14825    1.0274  1.86356     1.206   

               epidem    pandem       time      group    peopl  
misinfodem    1.16331  0.935609   0.835836    1.82751  1.21721  
covid         1.65599   1.24569    1.41466    2.09422  1.65426  
articl        1.11923  0.751603   0.708098    1.67686  1.10551  
commun        1.11811  0.941839   0.728133    1.83891  1.12996  
mechan       0.981829  0.805615   0.630183     1.7099  1.11525  
health        1.68828   1.41086      1.495    2.24603  1.88704  
strategi       1.0459  0.876623   0.619814    1.74272   1.1706  
psycholog     1.36759   1.17936    1.14433    2.04504  1.53597  
misinform     1.03317  0.850458   0.695799    1.76019  1.14825  
inform        1.10397  0.918664   0.758514    1.75681   1.0274  
2020          1.68517   1.21093    1.37622    2.23965  1.86356  
social        1.05316   1.32541   0.916963    1.78649    1.206  
epidem      3.332e-08   1.34219   0.984028    2.02774  1.54428  
pandem        1.34219         0    1.06184     1.9243  1.24942  
time         0.984028   1.06184  1.666e-08     1.7937  1.20018  
group         2.02774    1.9243     1.7937  3.332e-08  1.76392  
peopl         1.54428   1.24942    1.20018    1.76392        0  
Pairwise-distance
           misinfodem    covid    articl    commun       mechan       health  \
misinfodem          0  1.33926   2.07845   2.96288      2.96288      2.96288   
covid         1.33926        0   1.10881   1.65813      1.65813      1.65813   
articl        2.07845  1.10881         0   1.20521      1.20521      1.20521   
commun        2.96288  1.65813   1.20521         0            0            0   
mechan        2.96288  1.65813   1.20521         0            0  1.31709e-08   
health        2.96288  1.65813   1.20521         0  1.31709e-08            0   
strategi      2.55053  1.66321  0.554403   1.31385      1.31385      1.31385   
psycholog     2.55053  1.66321  0.554403   1.31385      1.31385      1.31385   
misinform     3.57135   2.2321   2.01252  0.819898     0.819898     0.819898   
inform        2.89364  1.70035  0.892838  0.538987     0.538987     0.538987   
2020          3.31627  2.07845   1.33926  0.554403     0.554403     0.554403   
social        4.01777  2.67852   2.41043   1.20521      1.20521      1.20521   
epidem              0        0         0         0            0            0   
pandem        4.01777  2.67852   2.41043   1.20521      1.20521      1.20521   
time          3.74442  2.47937   1.78568  0.850176     0.850176     0.850176   
group               0        0         0         0            0            0   
peopl         2.89364  1.70035  0.892838  0.538987     0.538987     0.538987   

            strategi psycholog misinform    inform      2020    social epidem  \
misinfodem   2.55053   2.55053   3.57135   2.89364   3.31627   4.01777      0   
covid        1.66321   1.66321    2.2321   1.70035   2.07845   2.67852      0   
articl      0.554403  0.554403   2.01252  0.892838   1.33926   2.41043      0   
commun       1.31385   1.31385  0.819898  0.538987  0.554403   1.20521      0   
mechan       1.31385   1.31385  0.819898  0.538987  0.554403   1.20521      0   
health       1.31385   1.31385  0.819898  0.538987  0.554403   1.20521      0   
strategi           0         0   2.12281  0.819898   1.20521   2.45969      0   
psycholog          0         0   2.12281  0.819898   1.20521   2.45969      0   
misinform    2.12281   2.12281         0   1.31385   1.07797  0.446419      0   
inform      0.819898  0.819898   1.31385         0  0.446419    1.6398      0   
2020         1.20521   1.20521   1.07797  0.446419         0   1.31385      0   
social       2.45969   2.45969  0.446419    1.6398   1.31385         0      0   
epidem             0         0         0         0         0         0      0   
pandem       2.45969   2.45969  0.446419    1.6398   1.31385         0      0   
time         1.62217   1.62217  0.998224  0.892838  0.446419   1.07797      0   
group              0         0         0         0         0         0      0   
peopl       0.819898  0.819898   1.31385         0  0.446419    1.6398      0   

              pandem      time group        peopl  
misinfodem   4.01777   3.74442     0      2.89364  
covid        2.67852   2.47937     0      1.70035  
articl       2.41043   1.78568     0     0.892838  
commun       1.20521  0.850176     0     0.538987  
mechan       1.20521  0.850176     0     0.538987  
health       1.20521  0.850176     0     0.538987  
strategi     2.45969   1.62217     0     0.819898  
psycholog    2.45969   1.62217     0     0.819898  
misinform   0.446419  0.998224     0      1.31385  
inform        1.6398  0.892838     0            0  
2020         1.31385  0.446419     0     0.446419  
social             0   1.07797     0       1.6398  
epidem             0         0     0            0  
pandem             0   1.07797     0       1.6398  
time         1.07797         0     0     0.892838  
group              0         0     0            0  
peopl         1.6398  0.892838     0  1.31709e-08  

(-0.3387423017564536, 0.9532, -1.6910743913274404)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,10)

Pairwise-distance
              2020    covid    pandem   health    polici  psycholog  \
2020             0  1.64398   1.21093  1.63587   1.38208     1.6757   
covid      1.64398        0   1.24569  1.92675   1.26996     1.5689   
pandem     1.21093  1.24569         0  1.41086  0.788078    1.17936   
health     1.63587  1.92675   1.41086        0   1.46491    1.41224   
polici     1.38208  1.26996  0.788078  1.46491  8.33e-09     1.0708   
psycholog   1.6757   1.5689   1.17936  1.41224    1.0708  1.666e-08   
impact      1.4073  1.31599  0.946785   1.4662  0.602045    1.18626   
countri    1.81508  1.02665   1.09741  1.74074  0.976989    1.32184   
develop    1.38408  1.33065  0.678531  1.43251  0.368953    1.06407   
nation     1.43333  1.36377  0.891159  1.32253  0.424222    1.16005   
social      1.6997  1.63894   1.32541  1.67666   1.13705    1.40467   
epidem     1.68517  1.65599   1.34219  1.68828   1.03278    1.36759   
time       1.37622  1.41466   1.06184    1.495  0.649391    1.14433   
group      2.23965  2.09422    1.9243  2.24603   1.74913    2.04504   
peopl      1.86356  1.65426   1.24942  1.88704   1.13437    1.53597   

                impact    countri   develop    nation    social     epidem  \
2020            1.4073    1.81508   1.38408   1.43333    1.6997    1.68517   
covid          1.31599    1.02665   1.33065   1.36377   1.63894    1.65599   
pandem        0.946785    1.09741  0.678531  0.891159   1.32541    1.34219   
health          1.4662    1.74074   1.43251   1.32253   1.67666    1.68828   
polici        0.602045   0.976989  0.368953  0.424222   1.13705    1.03278   
psycholog      1.18626    1.32184   1.06407   1.16005   1.40467    1.36759   
impact     1.17804e-08    1.14809  0.537294  0.615702   1.38141    1.23832   
countri        1.14809  1.666e-08   1.02248   1.01999   1.41843    1.34755   
develop       0.537294    1.02248  8.33e-09  0.573437   1.17569    1.09679   
nation        0.615702    1.01999  0.573437         0   1.13367    1.06065   
social         1.38141    1.41843   1.17569   1.13367         0    1.05316   
epidem         1.23832    1.34755   1.09679   1.06065   1.05316  3.332e-08   
time          0.961153    1.20956  0.798896  0.623022  0.916963   0.984028   
group          1.85976    2.03729    1.7955   1.77705   1.78649    2.02774   
peopl          1.32558    1.49549   1.14474   1.16066     1.206    1.54428   

                time      group    peopl  
2020         1.37622    2.23965  1.86356  
covid        1.41466    2.09422  1.65426  
pandem       1.06184     1.9243  1.24942  
health         1.495    2.24603  1.88704  
polici      0.649391    1.74913  1.13437  
psycholog    1.14433    2.04504  1.53597  
impact      0.961153    1.85976  1.32558  
countri      1.20956    2.03729  1.49549  
develop     0.798896     1.7955  1.14474  
nation      0.623022    1.77705  1.16066  
social      0.916963    1.78649    1.206  
epidem      0.984028    2.02774  1.54428  
time       1.666e-08     1.7937  1.20018  
group         1.7937  3.332e-08  1.76392  
peopl        1.20018    1.76392        0  
Pairwise-distance
                  2020    covid       pandem   health       polici psycholog  \
2020       2.98023e-08  1.40859      1.40457  1.39359       1.1861   1.40269   
covid          1.40859        0      1.40314  1.39289      1.18071   1.39959   
pandem         1.40457  1.40314  2.98023e-08  1.38902      1.18354   1.39676   
health         1.39359  1.39289      1.38902        0      1.16427   1.38385   
polici          1.1861  1.18071      1.18354  1.16427  1.05367e-08   1.17642   
psycholog      1.40269  1.39959      1.39676  1.38385      1.17642         0   
impact         1.39073  1.38451      1.38414  1.37391      1.16903   1.38027   
countri        1.39416  1.39268      1.38846  1.37928      1.17603   1.38387   
develop        1.34013  1.33483      1.33611   1.3187       1.0975   1.33625   
nation         1.08223  1.07356      1.08874  1.06366     0.530187   1.08601   
social         1.38116  1.37955      1.37681  1.36128       1.1579   1.37572   
epidem         1.38005  1.37922       1.3742  1.36229      1.14678   1.36667   
time           1.38974  1.38462      1.37977  1.37021      1.16468   1.38332   
group          1.39081   1.3877      1.38526  1.37121      1.16239   1.38284   
peopl           1.4018  1.40042      1.39753  1.38745      1.17672   1.39234   

            impact  countri   develop    nation   social   epidem     time  \
2020       1.39073  1.39416   1.34013   1.08223  1.38116  1.38005  1.38974   
covid      1.38451  1.39268   1.33483   1.07356  1.37955  1.37922  1.38462   
pandem     1.38414  1.38846   1.33611   1.08874  1.37681   1.3742  1.37977   
health     1.37391  1.37928    1.3187   1.06366  1.36128  1.36229  1.37021   
polici     1.16903  1.17603    1.0975  0.530187   1.1579  1.14678  1.16468   
psycholog  1.38027  1.38387   1.33625   1.08601  1.37572  1.36667  1.38332   
impact           0  1.37441   1.33717   1.06999  1.35463  1.35559  1.36633   
countri    1.37441        0   1.30759   1.07975   1.3601   1.3684  1.37383   
develop    1.33717  1.30759         0  0.906651  1.31014  1.29463  1.31101   
nation     1.06999  1.07975  0.906651         0  1.05625  1.02749   1.0551   
social     1.35463   1.3601   1.31014   1.05625        0  1.34363  1.36331   
epidem     1.35559   1.3684   1.29463   1.02749  1.34363        0  1.35265   
time       1.36633  1.37383   1.31101    1.0551  1.36331  1.35265        0   
group      1.36958   1.3713   1.32219   1.05924  1.36177  1.35927  1.36874   
peopl      1.38121  1.38798   1.32331   1.08154  1.37895  1.37021  1.38017   

                 group        peopl  
2020           1.39081       1.4018  
covid           1.3877      1.40042  
pandem         1.38526      1.39753  
health         1.37121      1.38745  
polici         1.16239      1.17672  
psycholog      1.38284      1.39234  
impact         1.36958      1.38121  
countri         1.3713      1.38798  
develop        1.32219      1.32331  
nation         1.05924      1.08154  
social         1.36177      1.37895  
epidem         1.35927      1.37021  
time           1.36874      1.38017  
group      2.10734e-08       1.3823  
peopl           1.3823  1.49012e-08  

(0.5025992380324102, 0.0026, 2.045465473690431)

mantel_test_verticle(model_psy_p,model_psy_paragraph,10)

Pairwise-distance
                2020    covid    pandem       health polici psycholog  \
2020               0  2.07845   1.31385     0.554403      0   1.20521   
covid        2.07845        0   2.67852      1.65813      0   1.66321   
pandem       1.31385  2.67852         0      1.20521      0   2.45969   
health      0.554403  1.65813   1.20521            0      0   1.31385   
polici             0        0         0            0      0         0   
psycholog    1.20521  1.66321   2.45969      1.31385      0         0   
impact      0.538987  2.20461   1.83335     0.998224      0  0.892838   
countri            0        0         0            0      0         0   
develop     0.538987  2.20461   1.83335     0.998224      0  0.892838   
nation             0        0         0            0      0         0   
misinfodem   3.31627  1.33926   4.01777      2.96288      0   2.55053   
articl       1.33926  1.10881   2.41043      1.20521      0  0.554403   
commun      0.554403  1.65813   1.20521            0      0   1.31385   
mechan      0.554403  1.65813   1.20521  1.31709e-08      0   1.31385   
strategi     1.20521  1.66321   2.45969      1.31385      0         0   
misinform    1.07797   2.2321  0.446419     0.819898      0   2.12281   
inform      0.446419  1.70035    1.6398     0.538987      0  0.819898   

                 impact countri      develop nation misinfodem    articl  \
2020           0.538987       0     0.538987      0    3.31627   1.33926   
covid           2.20461       0      2.20461      0    1.33926   1.10881   
pandem          1.83335       0      1.83335      0    4.01777   2.41043   
health         0.998224       0     0.998224      0    2.96288   1.20521   
polici                0       0            0      0          0         0   
psycholog      0.892838       0     0.892838      0    2.55053  0.554403   
impact                0       0  6.58545e-09      0    3.31522   1.23968   
countri               0       0            0      0          0         0   
develop     6.58545e-09       0            0      0    3.31522   1.23968   
nation                0       0            0      0          0         0   
misinfodem      3.31522       0      3.31522      0          0   2.07845   
articl          1.23968       0      1.23968      0    2.07845         0   
commun         0.998224       0     0.998224      0    2.96288   1.20521   
mechan         0.998224       0     0.998224      0    2.96288   1.20521   
strategi       0.892838       0     0.892838      0    2.55053  0.554403   
misinform       1.61696       0      1.61696      0    3.57135   2.01252   
inform         0.554403       0     0.554403      0    2.89364  0.892838   

              commun       mechan  strategi misinform    inform  
2020        0.554403     0.554403   1.20521   1.07797  0.446419  
covid        1.65813      1.65813   1.66321    2.2321   1.70035  
pandem       1.20521      1.20521   2.45969  0.446419    1.6398  
health             0  1.31709e-08   1.31385  0.819898  0.538987  
polici             0            0         0         0         0  
psycholog    1.31385      1.31385         0   2.12281  0.819898  
impact      0.998224     0.998224  0.892838   1.61696  0.554403  
countri            0            0         0         0         0  
develop     0.998224     0.998224  0.892838   1.61696  0.554403  
nation             0            0         0         0         0  
misinfodem   2.96288      2.96288   2.55053   3.57135   2.89364  
articl       1.20521      1.20521  0.554403   2.01252  0.892838  
commun             0            0   1.31385  0.819898  0.538987  
mechan             0            0   1.31385  0.819898  0.538987  
strategi     1.31385      1.31385         0   2.12281  0.819898  
misinform   0.819898     0.819898   2.12281         0   1.31385  
inform      0.538987     0.538987  0.819898   1.31385         0  
Pairwise-distance
                   2020     covid       pandem    health       polici  \
2020        2.98023e-08   1.40859      1.40457   1.39359       1.1861   
covid           1.40859         0      1.40314   1.39289      1.18071   
pandem          1.40457   1.40314  2.98023e-08   1.38902      1.18354   
health          1.39359   1.39289      1.38902         0      1.16427   
polici           1.1861   1.18071      1.18354   1.16427  1.05367e-08   
psycholog       1.40269   1.39959      1.39676   1.38385      1.17642   
impact          1.39073   1.38451      1.38414   1.37391      1.16903   
countri         1.39416   1.39268      1.38846   1.37928      1.17603   
develop         1.34013   1.33483      1.33611    1.3187       1.0975   
nation          1.08223   1.07356      1.08874   1.06366     0.530187   
misinfodem      1.00964  0.998188      0.99604  0.990675     0.666441   
articl          1.25618   1.24364      1.23712   1.23471      0.99842   
commun           1.3593   1.35413      1.35723   1.33539       1.1385   
mechan         0.997926  0.993957     0.991672  0.975204     0.640464   
strategi        1.01514   1.01603      1.00632  0.994037     0.674641   
misinform      0.998371  0.998127      0.99557  0.974389     0.649831   
inform          1.34995   1.34921      1.34654    1.3382      1.11087   

           psycholog    impact   countri   develop    nation misinfodem  \
2020         1.40269   1.39073   1.39416   1.34013   1.08223    1.00964   
covid        1.39959   1.38451   1.39268   1.33483   1.07356   0.998188   
pandem       1.39676   1.38414   1.38846   1.33611   1.08874    0.99604   
health       1.38385   1.37391   1.37928    1.3187   1.06366   0.990675   
polici       1.17642   1.16903   1.17603    1.0975  0.530187   0.666441   
psycholog          0   1.38027   1.38387   1.33625   1.08601   0.998159   
impact       1.38027         0   1.37441   1.33717   1.06999   0.978871   
countri      1.38387   1.37441         0   1.30759   1.07975   0.990788   
develop      1.33625   1.33717   1.30759         0  0.906651    0.89179   
nation       1.08601   1.06999   1.07975  0.906651         0   0.470226   
misinfodem  0.998159  0.978871  0.990788   0.89179  0.470226          0   
articl       1.26125   1.21618   1.23717    1.1515  0.888794   0.762354   
commun       1.35026     1.338    1.3383   1.28142     1.044   0.952795   
mechan      0.985848  0.971771  0.974543  0.890443   0.43569   0.124054   
strategi     1.00808  0.993475   0.98493  0.873182  0.465704   0.164871   
misinform   0.991994  0.975257  0.982905  0.894889  0.444944   0.113261   
inform       1.34084   1.32679   1.32992    1.2774   1.01059   0.862982   

              articl       commun     mechan  strategi    misinform  \
2020         1.25618       1.3593   0.997926   1.01514     0.998371   
covid        1.24364      1.35413   0.993957   1.01603     0.998127   
pandem       1.23712      1.35723   0.991672   1.00632      0.99557   
health       1.23471      1.33539   0.975204  0.994037     0.974389   
polici       0.99842       1.1385   0.640464  0.674641     0.649831   
psycholog    1.26125      1.35026   0.985848   1.00808     0.991994   
impact       1.21618        1.338   0.971771  0.993475     0.975257   
countri      1.23717       1.3383   0.974543   0.98493     0.982905   
develop       1.1515      1.28142   0.890443  0.873182     0.894889   
nation      0.888794        1.044    0.43569  0.465704     0.444944   
misinfodem  0.762354     0.952795   0.124054  0.164871     0.113261   
articl             0      1.19818   0.754808  0.774005     0.764559   
commun       1.19818  2.10734e-08   0.927779  0.936536     0.919373   
mechan      0.754808     0.927779          0  0.173639    0.0847542   
strategi    0.774005     0.936536   0.173639         0     0.153989   
misinform   0.764559     0.919373  0.0847542  0.153989  1.86265e-09   
inform       1.19978      1.30395   0.892146   0.90969     0.888665   

                 inform  
2020            1.34995  
covid           1.34921  
pandem          1.34654  
health           1.3382  
polici          1.11087  
psycholog       1.34084  
impact          1.32679  
countri         1.32992  
develop          1.2774  
nation          1.01059  
misinfodem     0.862982  
articl          1.19978  
commun          1.30395  
mechan         0.892146  
strategi        0.90969  
misinform      0.888665  
inform      1.49012e-08  

(-0.12583881303579586, 0.7228, -0.6018039276259918)